image resolution

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

Image Processing, a field which is rapidly evolving, has its applications in various aspects of science and engineering. It is a method of performing some operations on image in order to get an enhanced image or to extract some useful information from it. Application of image processing is image inpainting whose purpose is to restore the image to create a better image. Image inpainting aims at filling in missing pixels in a large unknown region of an image in a visually plausible way. There are various methods proposed for image inpainting. The challenging area of current inpainting algorithms is to restore both texture and edge characteristic information for large and thick damaged regions in an image. Merging the missing region with its surroundings and preserving both texture and structure information are the research key of image inpainting.In this paper a survey is done on various image inpainting methods.

This research introduces CTsGAN, a novel Generative Adversarial Network tailored for enhancing the perceptual quality of CT scan images obtained from portable scanners. Building upon the foundations of super-resolution GANs and inspired by the Enhanced SRGAN (ESRGAN), CTsGAN addresses the increasing demand for highquality medical imaging in the context of portable CT scanners. The model employs a Kth Order Degradation Module to simulate real-world multi-level degradation, enhancing the robustness of the training process. The network architecture incorporates a modified Residual-in-Residual Dense Block with Batch Normalization for efficiency, a U-Net Discriminator with Spectral Normalization for discriminative accuracy, and a Perceptual Loss function for improved visual fidelity. The research also delves into the challenges posed by the mobile CT scanning environment, exploring a second-order degradation model to bridge the gap between synthetic and realistic degraded images. Additionally, the paper details the training process, web application deployment, and comparative evaluations against state-of-the-art methods, showcasing CTsGAN's superior performance in terms of sharpness, brightness, and detail preservation. The user-friendly web application, implemented using the Streamlit library, provides a seamless experience for medical professionals to enhance CT scan images and make informed decisions. As a contribution to the field of medical image enhancement, CTsGAN demonstrates potential applications in real-time scenarios and remains open for further development and contributions.

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated wi...

A super-resolution CMOS image sensor is introduced in this paper for bio-microfluidic imaging. Compared to the traditional scientific CCD or CMOS image sensor, our design achieves the balance between high-sensitivity and highresolution from system design perspective with the use of singleframe super-resolution. Moreover, the column-parallel image sensor architecture with correlated-double-sampling is deployed for high-speed and low-noise readout. The chip is designed in standard 0.18µm CMOS mixed-signal process with area of 2.5mm×5.0mm, 128×128 pixel-array and speed of 1750 frames/s. The resolution is improved by 4X by on-chip super-resolution algorithm while large pixel (10µm×10µm) is employed in image sensor for high-sensitivity.

Edge illumination (EI) X-ray phase-contrast imaging (XPCI) has potential for applications in different fields of research, including materials science, non-destructive industrial testing, small-animal imaging, and medical imaging. One of its main advantages is the compatibility with laboratory equipment, in particular with conventional non-microfocal sources, which makes its exploitation in normal research laboratories possible. In this work, we demonstrate that the signal in laboratory implementations of EI can be correctly described with the use of the simplified geometrical optics. Besides enabling the derivation of simple expressions for the sensitivity and spatial resolution of a given EI setup, this model also highlights the EI's achromaticity. With the aim of improving image quality, as well as to take advantage of the fact that all energies in the spectrum contribute to the image contrast, we carried out EI acquisitions using a photon-counting energy-resolved detector. The obtained results demonstrate that this approach has great potential for future laboratory implementations of EI.

A sensor that is beneficial to inspect small devices has been missing in life science as well as industry. Frequency modulated continuous wave (FMCW) optical sensing system has been successful in profiling the image of a coin and a printed circuit board with the measurement accuracy of 3σ = 29 µm in 10 minutes by using a DFB laser. Here we propose and demonstrate a faster profiling system using a VCSEL. We applied three conditions for high-speed FMCW optical sensing system, i.e. symmetric method with the different modulation frequency, modulation amplitude, and asymmetric method. The results show that asymmetric method is successful in profiling a coin object with a diameter of 2.25 cm in 23.13 seconds.

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.

Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. erefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning superresolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.

One of the first ISO digital camera standards to address image microstructure was ISO 12233, which introduced the SFR, spatial frequency response, based on the analysis of edge features in digital images. The SFR, whether derived from edges or periodic signals, describes the capture of image detail as a function of spatial frequency. Often during camera testing, however, there is an interest in distilling SFR results down to a single value that can be compared with acceptable tolerances. As a measure of limiting resolution, it has been suggested that the frequency at which the SFR falls to, e.g., 10%, can be used. We use this limiting resolution to introduce a sampling efficiency measure, being considered under the current ISO 12233 standard revision effort. The measure is the ratio of limiting resolution frequency to that implied by the image (sensor) sampling alone. The rationale and details of this measure are described, as are example measurements. One-dimensional sampling efficiency calculations for multiple directions are included in a two-dimensional analysis.

One of the first ISO digital camera standards to address image microstructure was ISO 12233, which introduced the SFR, spatial frequency response, based on the analysis of edge features in digital images. The SFR, whether derived from edges or periodic signals, describes the capture of image detail as a function of spatial frequency. Often during camera testing, however, there is an interest in distilling SFR results down to a single value that can be compared with acceptable tolerances. As a measure of limiting resolution, it has been suggested that the frequency at which the SFR falls to, e.g., 10%, can be used. We use this limiting resolution to introduce a sampling efficiency measure, being considered under the current ISO 12233 standard revision effort. The measure is the ratio of limiting resolution frequency to that implied by the image (sensor) sampling alone. The rationale and details of this measure are described, as are example measurements. One-dimensional sampling efficiency calculations for multiple directions are included in a two-dimensional analysis.

The well-established Modulation Transfer Function (MTF) is an imaging performance parameter that is well suited to describing certain sources of detail loss, such as optical focus and motion blur. As performance standards have developed for digital imaging systems, the MTF concept has been adapted and applied as the spatial frequency response (SFR). The international standard for measuring digital camera resolution, ISO 12233, was adopted over a decade ago. Since then the slanted edge-gradient analysis method on which it was based has been improved and applied beyond digital camera evaluation. Practitioners have modified minor elements of the standard method to suit specific system characteristics, unique measurement needs, or computational shortcomings in the original method. Some of these adaptations have been documented and benchmarked, but a number have not. In this paper we describe several of these modifications, and how they have improved the reliability of the resulting system evaluations. We also review several ways the method has been adapted and applied beyond camera resolution.

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Assessing the severity and prognosis of patients with craniocerebral damage is a major research area in medicine since it is a prevalent clinical disease. Acute craniocerebral injury, a common traumatic condition, is often caused by traffic accidents, collisions, and falls in daily life. Secondary craniocerebral injury refers to symptoms such as brain edema and intracranial hemorrhage after acute craniocerebral injury, which will aggravate the injury. Secondary craniocerebral injury can be avoided by effective and timely treatment, and real-time detection of brain edema and intracranial hemorrhage by non-invasive medical imaging is a solution. Therefore, non-invasive medical imaging technology has recently emerged as a new area of study. A new imaging technology, namely the brain injury detection technology based on electromagnetic induction, has been discovered after years of research on non-invasive detection of brain injury. Initially, electromagnetic induction technology was widely used in metal nondestructive testing. The human body, as a conductor, also has electromagnetic induction, allowing this technology to be used on the human body. This study reviews the technologies for detecting electromagnetic induction in cases of craniocerebral damage, including induced current electrical impedance tomography, magneto-acoustic tomography, and eddy current damping sensors for detection and imaging.

3D models of buildings are useful for many applications such as urban planning and environmental simulation, cartography, tourism and mobile navigation. Automatically generating building models in the form of 3D CAD representation is the major part of city modeling and a challenge for many researchers. Airborne laser-scanning (ALS) results into high-quality geometrical information about the landscape. It is suitable for the reconstruction of 3D objects like buildings because of its high density and geometrical accuracy. In this paper a novel approach is proposed for automatically generating 3D building models based on definition of Levels of Detail (LOD) in the CityGML standard. Three levels of detail are considered in this paper. In the first LOD (LOD0), the Digital Terrain Model extracted from LIDAR data is represented. For this purpose the Digital Surface Model is filtered using geodesic morphology. A prismatic model containing the major walls of the building is generated to form the LOD1. The building outlines are detected by classification of non-ground objects and the building outlines are approximated by two approaches; hierarchical fitting of Minimum Boundary rectangles (MBR) and RANSAC based straight line fitting algorithm. LOD2 is formed by including the roof structures into the model. For this purpose, a model driven approach based on the analysis of the 3D points in a 2D projection plane is proposed. A building region is divided into smaller parts according to the direction and the number of ridge lines, which are extracted using geodesic morphology. The 3D model is derived for each building part. Finally, a complete building model is formed by merging the 3D models of the building parts and adjusting the nodes after the merging process.

Hyperspectral (HS) image can describe subtle differences in the spectral signatures of materials, but it has low spatial resolution limited by the existing technical and budget constraints. In this paper, we propose a promising HS pansharpening method with deep priors (HPDP) to fuse a low-resolution (LR) HS image with a high-resolution (HR) panchromatic (PAN) image. Different from the existing methods, we redefine the spectral response function (SRF) based on the larger eigenvalue of structure tensor (ST) matrix for the first time that is more in line with the characteristics of HS imaging. Then, we introduce HFNet to capture deep residual mapping of high frequency across the upsampled HS image and the PAN image in a bandby-band manner. Specifically, the learned residual mapping of high frequency is injected into the structural transformed HS images, which are the extracted deep priors served as additional constraint in a Sylvester equation to estimate the final HR HS image. Comparative analyses validate that the proposed HPDP method presents the superior pansharpening performance by ensuring higher quality both in spatial and spectral domains for all types of data sets. In addition, the HFNet is trained in the high-frequency domain based on multispectral (MS) images, which overcomes the sensitivity of deep neural network (DNN) to data sets acquired by different sensors and the difficulty of insufficient training samples for HS pansharpening.

With Single Photon Emission Computed Tomography (SPECT), images of minute concentrations of tracer molecules can be acquired, allowing in vivo molecular imaging. For human imaging, the SPECT system has a modest spatial resolution of 5-15 mm, a large field of view and a high sensitivity. Using multi-pinhole SPECT, one can trade in field of view for resolution with preserved sensitivity, which enables the implementation of a small animal SPECT system with an improved resolution, currently ranging from 0.3 to 2 mm, in a much smaller field of view. The unconventional collimation and the more stringent resolution requirements pose problems that are not present in clinical SPECT imaging. This paper discusses how these problems can be solved to implement micro-SPECT imaging on a rotating gamma camera.

The aim of this study was to enhance high-sensitivity imaging of a limited field of view in mice using multipinhole collimators on a dual head clinical gamma camera. A fast analytical method was used to predict the contrast-to-noise ratio (CNR) in many points of a homogeneous cylinder for a large number of pinhole collimator designs with modest overlap. The design providing the best overall CNR, a configuration with 7 pinholes, was selected. Next, the pinhole pattern was made slightly irregular to reduce multiplexing artifacts. Two identical, but mirrored 7-pinhole plates were manufactured. In addition, the calibration procedure was refined to cope with small deviations of the camera from circular motion. First, the new plates were tested by reconstructing a simulated homogeneous cylinder measurement. Second, a Jaszczak phantom filled with 37 MBq 99m Tc was imaged on a dual head gamma camera, equipped with the new pinhole collimators. The image quality before and after refined calibration was compared for both heads, reconstructed separately and together. Next, 20 short scans of the same phantom were performed with single and multipinhole collimation to investigate the noise improvement of the new design. Finally, two normal mice were scanned using the new multipinhole designs to illustrate the reachable image quality of abdomen and thyroid imaging. The simulation study indicated that the irregular patterns suppress most multiplexing artifacts. Using body support information strongly reduces the remaining multiplexing artifacts. Refined calibration improved the spatial resolution. Depending on the location in the phantom, the CNR increased with a factor of 1 to 2.5 using the new instead of a single pinhole design. The first proof of principle scans and reconstructions were successful, allowing the release of the new plates and software for preclinical studies in mice.

The use of time-of-flight (TOF) information during positron emission tomography (PET) reconstruction has been found to improve the image quality. In this work we quantified this improvement using two existing methods: (1) a very simple analytical expression only valid for a central point in a large uniform disk source, and (2) efficient analytical approximations for post-filtered maximum likelihood expectation maximization (MLEM) reconstruction with a fixed target resolution, predicting the image quality in a pixel or in a small region of interest based on the Fisher information matrix. Using this latter method the weighting function for filtered backprojection reconstruction of TOF PET data proposed by C. Watson can be derived. The image quality was investigated at different locations in various software phantoms. Simplified as well as realistic phantoms, measured both with TOF PET systems and with a conventional PET system, were simulated. Since the time resolution of the system is not always accurately known, the effect on the image quality of using an inaccurate kernel during reconstruction was also examined with the Fisher information-based method. First, we confirmed with this method that the variance improvement in the center of a large uniform disk source is proportional to the disk diameter and inversely proportional to the time resolution. Next, image quality improvement was observed in all pixels, but in eccentric and high-count regions the contrast-to-noise ratio (CNR) increased less than in central and low-or medium-count regions. Finally, the CNR was seen to decrease when the time resolution was inaccurately modeled (too narrow or too wide) during reconstruction. Although the maximum CNR is not very sensitive to the time resolution error, using an inaccurate TOF kernel tends to introduce artifacts in the reconstructed image.

This study aimed to develop a new method for combining Sentinel-2 and PlanetScope imagery. The normalized difference vegetation indices (NDVI) data were retrieved from the Earth observation satellites S2 Level-2A and PS Level-3 surface reflectance (PS) during the soybean growing phase. The proposed method utilizes the pyDMS algorithm, which is a decision treebased technique for enhancing low and high-resolution images with information from large-scale images. The robustness and flexibility of a multidimensional data fusion, deep neural network, and machine learning-based yield estimation model were analyzed based on the within-field variability in soybean yield. A comparative analysis revealed that the fusion data with 1.5 to 2.5 tons significantly outperformed individual predictions, demonstrating higher accuracy and fewer errors. The fusion data used to predict yields showed relatively small error ranges of 0.5 to 0.2 t/ha. In contrast, the PS and S2 datasets showed higher prediction errors. The study employed vegetation indices, and during validation, crop forecasts were compared using an NDVI map. The effectiveness of artificial neural networks in predicting crop yields was highlighted, demonstrating superior performance across diverse datasets compared with other algorithms. This novel fusion technique is essential for monitoring crop health and growth, improving agricultural practices, such as fertilization and water management, and improving yield forecast accuracy. This study provides valuable insights into phenology monitoring, image fusion accuracy, and the effectiveness of machine learning algorithms in predicting crop yields, emphasizing the benefits of fused imagery.

Object To investigate the potential of a clinical 3 T scanner to perform MRI of small rodents. Materials and methods Different dedicated small animal coils and several imaging sequences were evaluated to optimize image quality with respect to SNR, contrast and spatial resolution. As an application, optimal grey-whitematter contrast and resolution were investigated for rats. Furthermore, manganese-enhanced MRI was applied in mice with unilateral crush injury of the optic nerve to investigate coil performance on topographic mapping of the visual projection. Results Differences in SNR and CNR up to factor 3 and more were observed between the investigated coils. The best grey-white matter contrast was achieved with a high resolution 3D T 2 -weighted TSE (SPACE) sequence. Delineation of the retino-tectal projection and detection of defined visual pathway damage on the level of the optic nerve could be achieved by using a T 1 -weighted, 3D gradient echo sequence with isotropic resolution of (0.2 mm) 3 . Conclusions Experimental studies in small rodents requiring high spatial resolution can be performed by using a clinical 3 T scanner with appropriate dedicated coils.

This paper evaluates the performance of spatial methods to estimate leaf area index (LAI) fields from ground-based measurements at high-spatial resolution over a cropland landscape. Three geostatistical model variants of the kriging technique, the ordinary kriging (OK), the collocated cokriging (CKC) and kriging with an external drift (KED) are used. The study focused on the influence of the spatial sampling protocol, auxiliary information, and spatial resolution in the estimates. The main advantage of these models lies in the possibility of considering the spatial dependence of the data and, in the case of the KED and CKC, the auxiliary information for each location used for prediction purposes. A high-resolution NDVI image computed from SPOT TOA reflectance data is used as an auxiliary variable in LAI predictions. The CKC and KED predictions have proven the relevance of the auxiliary information to reproduce the spatial pattern at local scales, proving the KED model to be the best estimator when a non-stationary trend is observed. Advantages and limitations of the methods in LAI field predictions for two systematic and two stratified spatial samplings are discussed for high (20 m), medium (300 m) and coarse (1 km) spatial scales. The KED has exhibited the best observed local accuracy for all the spatial samplings. Meanwhile, the OK model provides comparable results when a well stratified sampling scheme is considered by land cover.

This paper introduces a novel large dataset for examplebased single image super-resolution and studies the stateof-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-ofthe-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.

Background: Infrared reflectography (IRR) remains an important method to visualize underdrawing and compositional changes in paintings. Older IRR camera systems are being replaced with near-infrared cameras consisting of room temperature infrared detector arrays made out of indium gallium arsenide (InGaAs) that operate over the spectral range of ~900 to 1700 nm. Two camera types are becoming prevalent. The first is staring array infrared cameras having 0.25-1 Megapixels where the camera or painting is moved to acquire tens of individual images that are later mosaicked together to create the infrared reflectogram. The second camera type is scanning back cameras in which a small InGaAs array (linear or area array) is mechanically scanned over a large image formed by the camera lens to create the reflectogram, typically 16 Megapixels. Both systems have advantages and disadvantages. The staring IR array cameras offer more flexible collection formats, provide live images, and allow for the use of spectral bandpass filters that can provide reflectograms with better contrast in some cases. They do require a mechanical system for moving the camera or the artwork and post-capture image mosaicking. Scanning back cameras eliminate or reduce the amount of mosaicking and movement of the camera, however the need to minimize light exposure to the artwork requires short integration times, and thus limits the use of spectral bandpass filters. In general, InGaAs cameras are not sensitive in the 1700 to ~2300 nm spectral region, which has been identified in prior studies as useful for examining paintings with copper green pigments or thick lead white paints. Prior studies using cameras with sensitivity from 1000 to 2500 nm have found in general the performance at wavelengths longer than 1700 nm degraded relative to the performance at shorter wavelengths. Thus, there is interest in a camera system having improved performance out to 2500 nm that can utilize spectral bandpass filters. Design requirements for such an improved IRR camera system were determined by first re-examining the optimal spectral window for detecting underdrawing. Thus the wavelength dependence of the clarity of carbon black underdrawings on a chalk ground covered by various paint swatches was measured from 750 to 2500 nm. Second, analysis of the loss of light transmission (1000-2500 nm) and the impact of thermal radiation (3000-5000 nm) on the performance of IR arrays sensitive to the 1000-5000 nm region was analyzed. From the results of these studies, a high sensitivity, near-infrared (1000-2450 nm) indium antimonide (InSb) staring array camera with a cold filter that blocked light >2450 nm was constructed with a color-corrected macro lens capable of holding interference filters. The camera system was characterized in three spectral bands (1100-1400, 1500-1800, and 2100-2400 nm) using test targets and art objects.

The effects of k-space sampling and signal decay on the effective spatial resolution of MRI and functional MRI (fMRI) are commonly assessed by means of the magnitude point-spread function (PSF), defined as the absolute values (magnitudes) of the complex MR imaging PSF. It is commonly assumed that this magnitude PSF signifies blurring, which can be quantified by its full-width at half-maximum (FWHM). Here we show that the magnitude PSF fails to accurately represent the true effects of k-space sampling and signal decay. Firstly, a substantial part of the width of the magnitude PSF is due to MRI sampling per se. This part is independent of any signal decay and its effect depends on the spatial frequency composition of the imaged object. Therefore, it cannot always be expected to introduce blurring. Secondly, MRI reconstruction is typically followed by taking the absolute values (magnitude image) of the reconstructed complex image. This introduces a non-linear stage into the process of image formation. The complex imaging PSF does not fully describe this process, since it does not reflect the stage of taking the magnitude image. Its corresponding magnitude PSF fails to correctly describe this process, since convolving the original pattern with the magnitude PSF is different from the true process of taking the absolute following a convolution with the complex imaging PSF. Lastly, signal decay can have not only a blurring, but also a high-pass filtering effect. This cannot be reflected by the strictly positive width of the magnitude PSF. As an alternative, we propose to first approximate the MRI process linearly. We then model the linear approximation by decomposing it into a signal decay-independent MR sampling part and an approximation of the signal decay effect. We approximate the latter as a convolution with a Gaussian PSF or, if the effect is that of high-pass filtering, as reversing the effect of a convolution with a Gaussian PSF. We show that for typical high-resolution fMRI at 7 Tesla, signal decay in Spin-Echo has a moderate blurring effect (FWHM = 0.89 voxels, corresponds to 0.44 mm for 0.5 mm wide voxels). In contrast, Gradient-Echo acts as a moderate high-pass filter that can be interpreted as reversing a Gaussian blurring with FWHM = 0.59 voxels (0.30 mm for 0.5 mm wide voxels). Our improved approximations and findings hold not only for Gradient-Echo and Spin-Echo fMRI but also for GRASE and VASO fMRI. Our findings support the correct planning, interpretation, and modeling of highresolution fMRI.

This report highlights the combination of the MicroTime 100 upright confocal fluorescence lifetime microscope with a Single Quantum Eos Superconducting Nanowire Single-Photon Detector (SNSPD) system as a powerful tool for photophysical research and applications. We focus on an application in materials science, photoluminescence imaging, and lifetime characterization of Cu(InGa)Se2 (CIGS) devices intended for solar cells. We demonstrate improved sensitivity, signal-to-noise ratio, and time-resolution in combination with confocal spatial resolution in the near-infrared (NIR) range, specifically in the 1000–1300 nm range. The MicroTime 100–Single Quantum Eos system shows two orders of magnitude higher signal-to-noise ratio for CIGS devices' photoluminescence imaging compared to a standard NIR-photomultiplier tube (NIR-PMT) and a three-fold improvement in time resolution, which is now limited by the laser pulse width. Our results demonstrate the advantages in terms of image quality ...

We designed a dedicated multi-detector multipinhole brain SPECT scanner to generate images of higher quality compared to general-purpose systems. The system, AdaptiSPECT-C, is intended to adapt its sensitivity-resolution trade-off by varying its aperture configurations allowing both high-sensitivity dynamic and high-spatial-resolution static imaging. The current system design consists of 23 detector heads arranged in a truncated spherical geometry. In this work, we investigated the axial and angular sampling capability of the current stationary system design. Two data acquisition schemes using limited rotation of the gantry and two others using axial translation of the imaging bed were also evaluated concerning their impact on image quality through improved sampling. Increasing both angular and axial sampling in the current prototype system resulted in quantitative improvements in image quality metrics and qualitative appearance of the images as determined in studies with specifically selected phantoms. Visual improvements for the brain phantoms with clinical distributions were less pronounced but presented quantitative improvements in the fidelity (normalized root-mean-square error (NRMSE)) and striatal specific binding ratio (SBR) for a dopamine transporter (DAT) distribution, and in NRMSE and activity recovery for a brain perfusion distribution. More pronounced improvements with increased sampling were seen in contrast recovery coefficient, bias, and coefficient of variation for a lesion in the brain perfusion distribution. The negligible impact of the most cranial ring of detectors on axial sampling, but its significant impact on sensitivity and angular sampling in the cranial portion of the imaging volume-of-interest were also determined.

The purpose of this paper is to study the structural properties of the reconstructed bone around a prosthetic device with submicrometer spatial resolution. In particular the X-ray technique illustrated hère allows to study the interface between a Zr prosthetic device and the new formed bone. The interface région formed between the device and the bone, is analysed with a spatial resolution of 0. 5 micron. Obtained results gave interesting information on Zr déformation and on crystallographic phase, grain size and texture of the bone which starts to grow in this region. From the study it appears évident a marked différence in the structure of reconstructed bone with respect to the native one.

The purpose of this paper is to study the structural properties of the reconstructed bone around a prosthetic device with submicrometer spatial resolution. In particular the X-ray technique illustrated hère allows to study the interface between a Zr prosthetic device and the new formed bone. The interface région formed between the device and the bone, is analysed with a spatial resolution of 0. 5 micron. Obtained results gave interesting information on Zr déformation and on crystallographic phase, grain size and texture of the bone which starts to grow in this region. From the study it appears évident a marked différence in the structure of reconstructed bone with respect to the native one.

This thesis presents the development of a high resolution wide field speckle illuminated interferometric surface plasmon microscope. This system is based on a Linnik interferometer and uses a high NA oil immersion objective for the excitation of surface plasmons. The confocal response of the microscope and high surface field enhancement due to surface plasmon excitation lead to high resolution and high contrast of weak surface structures and films. The microscope was also demonstrated in two other modes of operation, a sensing mode where the distributions seen in the back focal plane of the sample objective could be used to determine the optical parameters of the sample and a conventional surface plasmon microscope mode where image enhancement was created through the use of polarizers and optical masks. Results are presented that show the microscope as capable of imaging results comparable or better than that in the literature and without the need to scan. Hence the system is shown to be wholly suitable to biological and research fields interested in thin films and surface reactions. thank Mike Somekh, my supervisor, for many enlightening, vibrant and occasionally heated discussions on the topic of my thesis. Without his vast experience and guiding hand I would not have been able to produce this work. Amongst my collegues I would like to thank Ian Stockford, a fellow student and lab mate for making the hard times bearable, for discussing ideas and of course for answering my stupid questions. I would also like to thank Nick Sawyer, Mark Pitter, Steve Sharples and Dr See for disscussion on subjects as diverse as speckle illumination and thesis writing. Amongst the departmental staff I would particularly like to thank Kevin Last, Malcolm Gibbons and Ron Whitehead for providing the necessary help in building the mechanical assemblies for the experiment and for teaching me many workshop skills. I would also like to thank Rod Dykeman for preparing some of the samples used in the experiments. Thanks also go to some of my friends who have made the latter portions of the PhD experience most enjoyable, they include Paul Robson, Andrew Goodman, Steve Bull, Phil Bream and Andreas Pain. Last but not least I would like to thank my Mum who has supported me all the way through my academic career, good times and bad, without whose support I would have given up long ago.

Surface Plasmon microscopy can measure local changes of refractive index on the micron scale. Interferometric plasmon imaging delivers quantitative high spatial resolution sensitive to refractive index. In addition the so called V(z) method allows image contrast to be controlled by varying the sample defocus without substantially degrading spatial resolution. Here, we show how a confocal system provides a simpler and more stable alternative. This system, however, places greater demands on the dynamic range of the system. We therefore use a spatial light modulator to engineer the microscope pupil function to suppress light that does not contribute to the signal.

This thesis presents the development of a high resolution wide field speckle illuminated interferometric surface plasmon microscope. This system is based on a Linnik interferometer and uses a high NA oil immersion objective for the excitation of surface plasmons. The confocal response of the microscope and high surface field enhancement due to surface plasmon excitation lead to high resolution and high contrast of weak surface structures and films. The microscope was also demonstrated in two other modes of operation, a sensing mode where the distributions seen in the back focal plane of the sample objective could be used to determine the optical parameters of the sample and a conventional surface plasmon microscope mode where image enhancement was created through the use of polarizers and optical masks. Results are presented that show the microscope as capable of imaging results comparable or better than that in the literature and without the need to scan. Hence the system is shown to be wholly suitable to biological and research fields interested in thin films and surface reactions. thank Mike Somekh, my supervisor, for many enlightening, vibrant and occasionally heated discussions on the topic of my thesis. Without his vast experience and guiding hand I would not have been able to produce this work. Amongst my collegues I would like to thank Ian Stockford, a fellow student and lab mate for making the hard times bearable, for discussing ideas and of course for answering my stupid questions. I would also like to thank Nick Sawyer, Mark Pitter, Steve Sharples and Dr See for disscussion on subjects as diverse as speckle illumination and thesis writing. Amongst the departmental staff I would particularly like to thank Kevin Last, Malcolm Gibbons and Ron Whitehead for providing the necessary help in building the mechanical assemblies for the experiment and for teaching me many workshop skills. I would also like to thank Rod Dykeman for preparing some of the samples used in the experiments. Thanks also go to some of my friends who have made the latter portions of the PhD experience most enjoyable, they include Paul Robson, Andrew Goodman, Steve Bull, Phil Bream and Andreas Pain. Last but not least I would like to thank my Mum who has supported me all the way through my academic career, good times and bad, without whose support I would have given up long ago.

The colder, the better'' seems to be the mantra for structural biology with structures now routinely solved by cooling protein samples to cryogenic temperatures. The improved resolution at low temperatures results from reduced radiation damage by x-rays and electrons to proteins than at room temperature. We have adapted a microfabricated liquid cell to protect liquid protein samples from the vacuum of a conventional TEM. For the first time, we report the ability to image macromolecular assemblies, the horseshoe crab acrosome and bacteriorhodopin, at room temperature and in liquid water. The limit in resolution approaches that reported for these structures in vitreous ice and at liquid nitrogen temperatures. More importantly, we measure the falloff of the highest intensity reflections from Fourier transforms of electron micrographs or from electron diffraction patterns of the samples and find that radiation damage is reduced not increased in liquid water compared to vitreous ice. We propose that the mechanism of radiation damage differs between the two conditions. Imaging protein in water opens the door to the possibility of studying protein dynamics in real time and to solve structures of protein without freezing.

Super-resolution image reconstruction has been one of the most active research areas in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low-resolution images that have been sub-sampled and displaced by different amounts of sub-pixel shifts. The algorithm is based on the MAP framework, solving the optimization by proposed iteration steps. At each iteration step, the regularization parameter is updated using the partially reconstructed image solved at the last step. The proposed algorithm is tested on synthetic images, and the reconstructed images are evaluated by the PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.

To minimize the dose applied to the specimens during imaging in a MeV ion microbeam we have investigated new concepts that allow collection of images with multi-resolution support. To test the concept, a set of reference PIXE microbeam images with well-characterised noise were segmented using both a direct down-sampling technique and wavelet decomposition. The results show both techniques could be used to select fields of view with <2% of the fluence required to collect a normal image, however, there is no compelling reason to select one technique over the other.

Purpose : Monte Carlo (MC) simulation in Nuclear Medicine is a powerful tool for modelling many physical phenomena which are difficult to track or to measure directly. MC simulation in SPECT/CT imaging is particularly suitable for optimizing the quantification of activity in a patient, and, consequently, the absorbed dose to each organ. To do so, it is mandatory validate MC results with real data acquired with gamma camera. The aim of this study was the validation of SIMIND Monte Carlo code for modelling a Siemens Symbia Intevo Excel SPECT-CT gamma camera both for 99m Tc and 177 Lu. Methods : Phantom experiments using 99m Tc and 177 Lu have been performed with the purpose to measure spatial resolution, sensitivity and evaluate the calibration factor (CF) and recovery coefficients (RC) from acquired data. The geometries used for 2D planar imaging were (1) Petri dish and (2) capillary source while for 3D volumetric imaging were (3) a uniform filled cylinder phantom and (4) a Jaszczack...

Leaf area index (LAI) is one of the surface parameters that has importance in climate, weather, and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High-Resolution Radiometer (AVHRR) imagery every 10 days at 1-km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor. To improve the LAI algorithms and validate these products, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer, and mixed forests, and in cropland. Common measurement standards using the commercial Tracing Radiation and Architecture of Canopies (TRAC) and LAI-2000 instruments were followed. Eight Landsat Thematic Mapper (TM) scenes at 30-m resolution were used to locate ground sites and to facilitate spatial scaling to 1-km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high-to coarse-resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50 -75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates.

Lung function is typically characterized by spirometer measurements, which do not offer spatially specific information. Imaging during exhalation provides spatial information but is challenging due to large movement over a short time. The purpose of this work is to provide a solution to lung imaging during forced expiration using accelerated MRI. The method uses radial golden angle stack-of-stars gradient echo acquisition and compressed sensing reconstruction. A technique for dynamic 3D imaging of the lungs from highly undersampled data is developed and tested on six subjects. This method takes advantage of image sparsity, both spatially and temporally, including the use of reference frames called bookends. Sparsity, with respect to total variation, and residual from the bookends, enables reconstruction from an extremely limited amount of data. Dynamic 3D images can be captured at sub-150 ms temporal resolution, using only three (or less) acquired radial lines per slice per timepoint. The images have a spatial resolution of 4.6 × 4.6 × 10 mm. Lung volume calculations based on image segmentation are compared to those from simultaneously acquired spirometer measurements. Dynamic lung imaging during forced expiration is made possible by compressed sensing accelerated dynamic 3D radial MRI.

This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical findings that standardized low-resolution electromagnetic tomography (sLORETA) has no location bias. We also find that the minimum-variance spatial filter does exhibit bias in the reconstructed location of a single source, but that this bias is eliminated by using the normalized lead field. We then focus on the comparison of sLORETA and the lead-field normalized minimum-variance spatial filter, and analyze the effect of noise on source location bias. We find that the signal-to-noise ratio (SNR) in the measurements determines whether the sLORETA reconstruction has source location bias, while the lead-field normalized minimum-variance spatial filter has no location bias even in the presence of noise. Finally, we compare the spatial resolution for sLORETA and the minimum-variance filter, and show that the minimum-variance filter attains much higher resolution than sLORETA does. The results of these analyses are validated by numerical experiments as well as by reconstructions based on two sets of evoked magnetic responses.

Volume 3, Issue 5, September-October 2014 Page 176 Abstract Steganography can be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. Steganography is now more important due to need of secure communication in this era of potential and vulnerable computer users. The essential idea of Secured APPM is to use the values of pixel pair as a reference coordinate and search or finds that coordinate within the newly and specially designed compact neighborhood set of this pixel pair according to a given message digit. Then that pixel pair is gets replaced by the searched coordinate to hide / conceal the digit. It also provide adjustable payload and allows users to select digits in any notational system for data embedding, and thus achieves a better image quality. As Secured APPM does not produce any artifacts in stego images the steganalysis results are similar to those of the cover images so it offers a secure c...

While activity recognition is a current focus of research the challenging problem of fine-grained activity recognition is largely overlooked. We thus propose a novel database of 65 cooking activities, continuously recorded in a realistic setting. Activities are distinguished by fine-grained body motions that have low inter-class variability and high intraclass variability due to diverse subjects and ingredients. We benchmark two approaches on our dataset, one based on articulated pose tracks and the second using holistic video features. While the holistic approach outperforms the posebased approach, our evaluation suggests that fine-grained activities are more difficult to detect and the body model can help in those cases. Providing high-resolution videos as well as an intermediate pose representation we hope to foster research in fine-grained activity recognition.

Capillaries, the smallest blood vessels, are the distal end of the vasculature where oxygen and nutrients are exchanged between blood and tissue. Hence, noninvasive imaging of capillaries and function in vivo has long been desired as a window to studying fundamental physiology, such as neurovascular coupling. Existing imaging modalities cannot provide the required sensitivity and spatial resolution. We present in vivo imaging of the microvasculature including single capillaries in mice using optical-resolution photoacoustic microscopy (OR-PAM) developed in our laboratory. OR-PAM provides a lateral resolution of 5 m and an imaging depth Ͼ0.7 mm.

This paper deals with the problem of reconstructing high-resolution text images from an incomplete set of undersampled, blurred, and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posteriori estimate of the high resolution image and the estimation of the parameters involved in the model. The method is tested on real text images and car plates, examining the impact of blurring and the number of available low resolution images on the final estimate.

This work proposes a fast inspection method for large planar and soft curved aeronautic components, named Fast Scanning, able to obtain more than 1000 images per second in continuous operation. Using arrays, this method is based on composing every B-scan image in real time with a single trigger event, which reduces the acquisition time by the amount of image lines. Differently from other approaches, Fast Scanning performs real-time parallel beamforming of all the A-scan lines that compose the image with an adaptive control of focal laws. The former provides well focused images, while the latter keeps the optimal focal laws under probe-part geometry variations. Experimental results show that phased array imaging and Fast Scanning yield similar image quality with regard to resolution, sensitivity and signal-to-noise ratio. However, Fast Scanning provides one to two orders of magnitude higher frame rates, opening an opportunity to significantly reduce the inspection time of large aircr...

Abstract: The results of more than 280 different experiments aimed at exploring the main features
and performances of a newly developed gamma imager, called iPIX, are summarized in this paper.
iPIX is designed to quickly localize radioactive sources while estimating the ambient dose equivalent
rate at the measurement point. It integrates a 1 mm thick CdTe detector directly bump-bonded to a
Timepix chip, a tungsten coded-aperture mask, and a mini RGB camera. It also represents a major
technological breakthrough in terms of lightness, compactness, usability, response sensitivity, and
angular resolution. As an example of its key strengths, an 241Am source with a dose rate of only
few nSv/h can be localized in less than one minute.