Super resolution

This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.

In applications that demand high resolution images, it is often not feasible nor sometimes possible to acquire images of such high resolution by using current CCD camera with its inherent resolution. Instead, image processing methods may be used to construct a high resolution image from multiple, degraded, low resolution images. This paper presented a novel robust high resolution reconstruction method that consists of a high accuracy hierarchical registration, interpolation and postprocessing. This algorithm is found to be effective, robust and shows significant improvement over conventional interpolation algorithms. The success of this algorithm is demonstrated through various simulated and real images.

In applications that demand high resolution images, it is often not feasible nor sometimes possible to acquire images of such high resolution by using current CCD camera with its inherent resolution. Instead, image processing methods may be used to construct a high resolution image from multiple, degraded, low resolution images. This paper presented a novel robust high resolution reconstruction method that consists of a high accuracy hierarchical registration, interpolation and postprocessing. This algorithm is found to be effective, robust and shows significant improvement over conventional interpolation algorithms. The success of this algorithm is demonstrated through various simulated and real images.

This paper proposes image super-resolution techniques with multi-channel convolutional neural networks. In the proposed method, output pixels are classified into K × K groups depending on their coordinates. Those groups are generated from separate channels of a convolutional neural network (CNN). Finally, they are synthesized into a K × K magnified image. This architecture can enlarge images directly without bicubic interpolation. Experimental results of 2×2, 3×3, and 4×4 magnifications have shown that the average PSNR for the proposed method is about 0.2 dB higher than that for the conventional SRCNN.

This paper presents a novel design framework for Photonic-Optic Meta-Lattices (POML), engineered to harness negative refraction phenomena in plasmonic metals such as gold and silver. By integrating advanced nano-fabrication techniques with electromagnetic modeling, the study demonstrates how carefully optimized meta-lattice structures can achieve ultra-high-quality resonances and unprecedented near-field enhancement. These properties enable super-resolution imaging and optical manipulation beyond the classical diffraction limit. The synergistic combination of dielectric lattices and plasmonic layers opens new pathways for scalable, tunable, and highly sensitive photonic devices, with potential applications in microscopy, sensing, and integrated photonics. This research underscores the transformative potential of POML architectures in advancing the capabilities of next-generation optical technologies.

The impact of rain weather in the images will make it complicated to distinguish in the environment surroundings using an outdoor camera. Moreover, single image plays important role in numerous areas such as in object recognition and detection, enhancement, noise removal and weather condition removal. Rainy weather of outdoor images and videos reduces the visibility, performance of computer vision algorithms and other outdoor activities, which use for extracting features and information from images. Most of the previous review papers focus on the video techniques and raindrops which adherent on the surface and in the images. This paper presents a review of restoration rain streaks detection and removal from single image which has different techniques used in video and includes the result of the implemented experiments using three images.

Image recovery of super resolution aims to recover a single high resolution image from one or more low resolution frames. It is an ill-posed problem when the solution does not exist or it is unique. Thus, we introduce the prior based on Pearson type VII density integrated with a Markov Random Field (MRF) model. We devise two different versions, one that acts on the pixel level and another one that acts on the entire image. Here we present our parameter estimation and evaluate our approach using qualitative measurement in both compressive measurement and classical super resolution. Our estimation is theoretically simple and easy to implement.

Compressive imaging and image super-resolution aim at recovering a high-resolution scene from its compressed or low resolution measurements. The main difficulty lies with the ill-posedness of the problem, and there is no consensus as to how best to formulate image models that can both impose smoothness and preserve the edges in the image. Here we develop a new image prior based on the Pearson type VII density integrated with a Markov Random Field model, which has desirable robustness properties. We develop a fully automated hyperparameter estimation procedure for this approach, which makes it advantageous in comparison with alternatives. Our recovery algorithm, although very simple to implement, it achieves statistically significant improvements over previous results in under-determined problem settings, and it is able to recover images that contain texture.

Distributed Video Coding is a new video coding paradigm that enables video codecs with reversed complexity. It is well-known that the performance of any distributed video coder is heavily dependent on the quality of the side information generated by motion-estimation at the decoder. As a continuation of previous works, in this paper we propose to use a super-resolution method to up-sample the non-key frames using the key frames as reference, in order to improve the side information generation in a distributed video coding scheme with spatial scalability. The framework, proposed in previous works, enables reduced encoding complexity by reduced spatial-resolution encoding of the non-reference frames, followed by Wyner-Ziv coding of the Laplacian residue. The side information generation is iterative, in the first iteration the high-frequency data of matching blocks from the key frames are added to the low-resolution blocks of the interpolated downsampled frames. Results are presented using the state-of-the-art video coding standard H.264/AVC.

A motion-based side-information generation scheme with semi super-resolution for a scalable Wyner-Ziv coder framework is introduced. It is known that the performance of any Wyner-Ziv coder is heavily dependent on the efficiency of the side-information generation. We propose an iterative block based scheme to generate a semi super-resolution frame using the past and future reference frames which should be coded at full-resolution. To enable this side-information generation the framework should allow for low encoding complexity, reducing the spatial resolution only in the non-reference frames. The enhancement layer is produced using a residual frame of the reduced resolution encoded frame. The decoder first decodes the low resolution base layer and iteratively generates the side-information, along with channel decoding, to obtain a higher quality version of the decoded frame. Results of the implementation of the framework using the motion-based side-information in the H.263+ and H.264 standards are presented.

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.

This correspondence presents an improved version of an algorithm designed to perform image restoration via nonlinear interpolative vector quantization (NLIVQ). The improvement results from using lapped blocks during the decoding process. The algorithm is trained on original and diffraction-limited image pairs. The discrete cosine transform is again used in the codebook design process to control complexity. Simulation results are presented which demonstrate improvements over the nonlapped algorithm in both observed image quality and peak signal-to-noise ratio. In addition, the nonlinearity of the algorithm is shown to produce super-resolution in the restored images.

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.

In this paper the problem of blind super-resolution of sparse signals using arbitrary sampling scheme and atomic lift is discussed. After comprehensive description on blind superresolution problem, it is shown that using Prolate Spheroidal Wave Functions (PSWFs), it is possible to derive a new SemiDefinite Program (SDP) for the blind super-resolution problem. Unlike the previous results, the newly proposed SDP can localize spikes without magnitude recovery. Several numerical simulations were conducted to compare the performance of the proposed method with the recent related research.

In this paper we present a super resolution Bayesian methodology for pansharpening of multispectral images. By following the hierarchical Bayesian framework, and by applying variational methods to approximate probability distributions this methodology is able to: a) incorporate prior knowledge on the expected characteristics of the multispectral images, b) use the sensor characteristics to model the observation process of both panchromatic and multispectral images, c) include information on the unknown parameters in the model in the form of hyperprior distributions, and d) estimate the parameters of the hyperprior distributions on the unknown parameters together with the unknown parameters, and the high resolution multispectral image. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively.

Multiframe image super-resolution is a technique to obtain a high-resolution image by fusing a sequence of low-resolution ones. This paper deals with a new approach to robust super resolution based on regularization framework. Since registration is an important step that ensures the success of super resolution algorithms, must choose the most suitable method. We suggest a new algorithm specified at low resolution images with small deformations using fourth-order partial differential equations (PDE) regularization in the last step of super resolution. The deformations are not parametric and differs from one image to another. We use a curvature registration specially because image are slightly deformed. Experimental results show the robustness of the proposed method compared to classical super resolution methods.

In this paper, we consider the image super-resolution (SR) reconstitution problem. The main goal consists of obtaining a high-resolution (HR) image from a set of low-resolution (LR) ones. For that, we propose a novel approach based on a regularized criterion. The criterion is composed of the classical generalized total variation (TV) but adding a bilateral filter (BTV) regularizer. The main goal of our approach consists of the derivation and the use of an efficient combined deblurring and denoising stage that is applied on the high-resolution image. We demonstrate the existence of minimizers of the combined variational problem in the bounded variation space, and we propose a minimization algorithm. The numerical results obtained by our approach are compared with the classical robust super-resolution (RSR) algorithm and the SR with TV regularization. They confirm that the proposed combined approach allows to overcome efficiently the blurring effect while removing the noise.

Due to its vast range of applications, like encoding of video, effects of slow motion, an improvement of video, VFI (video frame interpolation) is a greatly researched issue. By enhancing the common quality of an image for complex series with high motion, occlusions, and dynamic texture, the techniques of VFI can be evolved. This conventional approach of a study ignores the fact that both background and foreground zones have diverse significance in perceptual quality of an image. Furthermore, creation of an accurate object can be crucial for the applications of computer vision. This study focuses to assess the methods of an interpolation and recognize the most effective one. The video frames were interpolated using three different methods. In order to eliminate noise before beginning the interpolation procedure, the suggested approach additionally included a median filter. Nearest Neighbor, Bilinear, and Bicubic interpolation techniques were used to find the most accurate interpolation. "Nearest Neighbor" was utilized for the first strategy, "Bilinear" for the second, and "Bicubic" for the third. Nearest neighbor is the easiest to use and fastest to process. The nearest neighbor algorithm calculates the value of the nearest pixel by approximating the coordinates of the intended interpolation point. Using this technique, every pixel in the final image is compared to its nearest equivalent pixel in the original (source) image. The basic concept is that bilinear interpolation does linear interpolation in two directions. Visual distortion is reduced by the bilinear interpolation algorithm's fractional zoom computation. The fundamental idea of bicubic interpolation is that a third-order polynomial function is used to fit a surface between four corner points. The intensity values and the horizontal, vertical, and diagonal derivates at the four corner locations must be given in order to compute a bicubic interpolation. After a comparison across multiple movies, it was shown that the bicubic technique is best technique produced additional sharper frames and had a little rate of errors than the nearest neighbor method and bilinear. For instance, the girl frame's entropy value was-3016.9593 when using bicubic technique while it was-4349.6685 when using bilinear and-6110.5119 when using Nearest Neighbor; For red band for example of girl frame the value of MSE was 709.684 when using bicubic technique while it was 945.547 when using bilinear and 1234.901 when using Nearest Neighbor.

This paper develops a mathematical theory of super-resolution. Broadly speaking, superresolution is the problem of recovering the fine details of an object-the high end of its spectrumfrom coarse scale information only-from samples at the low end of the spectrum. Suppose we have many point sources at unknown locations in [0, 1] and with unknown complex-valued amplitudes. We only observe Fourier samples of this object up until a frequency cut-off f c . We show that one can super-resolve these point sources with infinite precision-i.e. recover the exact locations and amplitudes-by solving a simple convex optimization problem, which can essentially be reformulated as a semidefinite program. This holds provided that the distance between sources is at least 2/f c . This result extends to higher dimensions and other models. In one dimension for instance, it is possible to recover a piecewise smooth function by resolving the discontinuity points with infinite precision as well. We also show that the theory and methods are robust to noise. In particular, in the discrete setting we develop some theoretical results explaining how the accuracy of the super-resolved signal is expected to degrade when both the noise level and the super-resolution factor vary.

This paper presents an approach for achieving full optical photoacoustic imaging with enhanced resolution utilizing speckle pattern analysis. The proposed technique involves projecting patterns derived from binary masks corresponding to orthogonal functions onto the target to elicit a photoacoustic signal. The resulting signal is then recorded using a high-speed camera and analyzed using correlation analysis of the speckle motion. Our results demonstrate the feasibility of this optical approach to achieve imaging with enhanced resolution without the need for physical contact with the target, opening up new possibilities for non-invasive medical imaging and other applications.

In this paper, some detection-theoretic, estimation-theoretic, and information-theoretic methods are investigated to analyze the problem of determining resolution limits in imaging systems. The canonical problem of interest is formulated based on a model of the blurred image of two closely spaced point sources of unknown brightness. To quantify a measure of resolution in statistical terms, the following question is addressed: "What is the minimum detectable separation between two point sources at a given signal-to-noise ratio (SNR), and for prespecified probabilities of detection and false alarm ( and )?". Furthermore, asymptotic performance analysis for the estimation of the unknown parameters is carried out using the Cramér-Rao bound. Although similar approaches to this problem (for one-dimensional (1-D) and oversampled signals) have been presented in the past, the analyzes presented in this paper are carried out for the general two-dimensional (2-D) model and general sampling scheme. In particular the case of under-Nyquist (aliased) images is studied. Furthermore, the Kullback-Liebler distance is derived to further confirm the earlier results and to establish a link between the detection-theoretic approach and Fisher information. To study the effects of variation in point spread function (PSF) and model mismatch, a perturbation analysis of the detection problem is presented as well.

The present paper is concerned with the statistical analysis of the resolution limit in a so-called "diffraction-limited" imaging system. The canonical case study is that of incoherent imaging of two closely-spaced sources of possibly unequal brightness. The objective is to study how far beyond the classical Rayleigh limit of resolution one can reach at a given signal to noise ratio. The analysis uses tools from statistical detection and estimation theory. Specifically, we will derive explicit relationships between the minimum detectable distance between two closely-spaced point sources imaged incoherently at a given SNR. For completeness, asymptotic performance analysis for the estimation of the unknown parameters is carried out using the Cramér-Rao bound. To gain maximum intuition, the analysis is carried out in one dimension, but can be well extended to the two-dimensional case and to more practical models.

This work focuses on the strong nonlinear saturation absorption (NSA)-induced optical super-resolution effect. A multi-layer system model is proposed to understand the strong NSA-induced formation of an optical pinhole channel and the generation of a super-resolution spot. Taking a Sb2Te3 thin film as an example, numerical simulations were conducted. The results illustrate that an optical pinhole channel is clearly formed by the NSA characteristics. This pinhole channel is similar to a near-field light probe. Light travels through the pinhole channel, and a super-resolution spot is generated at its apex. The near-field spot scanning experimental results show that the reduction ratio of the spot is approximately 44.8%, which is basically consistent with the numerical simulation result of 43%. This work is helpful for understanding optical nonlinear super-resolution effects and developing nanolithography, nanodata storage, high-resolution optical imaging technologies with nonlinear th...

El cerebro humano es el órgano de mayor complejidad en nuestro organismo y a su vez el que mayor fascinación ha generado durante el último siglo, despertando generaciones de investigadores, quienes a pesar de describir detalladamente la macro-anatomía reconocen lo poco que se sabe sobre su función y micro-arquitectura (Gray, 1918, Raichle and. Es por ello que para contribuir a la comprensión del funcionamiento cerebral es necesario dilucidar como éste conduce el impulso electro-químico y es capaz de conectar diferentes áreas neuronales según el tipo de información transportada. A escala microscópica, las neuronas transmiten la información a otras células neuronales a través de su axón. Los axones representan la fibra nerviosa, los que unidos a otras fibras forman paquetes nerviosos denominados

El cerebro humano es el órgano de mayor complejidad en nuestro organismo y a su vez el que mayor fascinación ha generado durante el último siglo, despertando generaciones de investigadores, quienes a pesar de describir detalladamente la macro-anatomía reconocen lo poco que se sabe sobre su función y micro-arquitectura (Gray, 1918, Raichle and. Es por ello que para contribuir a la comprensión del funcionamiento cerebral es necesario dilucidar como éste conduce el impulso electro-químico y es capaz de conectar diferentes áreas neuronales según el tipo de información transportada. A escala microscópica, las neuronas transmiten la información a otras células neuronales a través de su axón. Los axones representan la fibra nerviosa, los que unidos a otras fibras forman paquetes nerviosos denominados

In this paper, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this paper are as follows: First, by applying concise but effective concatenation and feature selection mechanism, we establish a novel connection mechanism which connect different modules in the modules stacking network. Second, both pixel-wise and channelwise attention mechanisms are used in each module convolution layer, which promotes further extraction of more essential information in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising.

In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-...

In this article, a novel superresolution mapping (SRM) method based on pixel-, subpixel-, and superpixel-scale spatial dependence (PSSSD) with pansharpening technique is proposed. First, an original coarse resolution remote sensing image and a high-resolution panchromatic image are fused by the pansharpening technique to produce a pansharpened result. A segmentation image with numerous superpixels, which represent irregular objects, is generated by adaptively segmenting the pansharpened result. Second, the class proportions of pixels, subpixels, and superpixels are, respectively, obtained from the original image, the pansharpened image, and the segmentation image. Pixel-scale spatial dependence is obtained using a pixel spatial attraction model. Subpixel-scale spatial dependence is derived using a novel subpixel spatial attraction model. Superpixel-scale spatial dependence is obtained through an extended random walker algorithm. Third, the three-scale spatial dependence is integrated into the PSSSD. Finally, a class allocation method based on object units is used to obtain the SRM results according to the PSSSD. Experimental results for three remote sensing images show that the proposed PSSSD outperforms the existing state-of-the-art SRM methods.

In this paper, the three-dimensional problem of detection and localization of buried metallic objects inside a multilayered medium is studied. Decomposition of the time-reversal operator (DORT) is used as the inversion algorithm in the imaging method. In this approach, one has to analyze the time-reversal (TR) operator obtained by using the multistatic data matrix (MDM) of a TR array. To attain MDM in the forward problem, full-wave simulation results from a commercial simulation software are used. For synthetic retransmission of signals in this inverse problem, determination of the appropriate medium Green's function is required. Here, for the first time the complex images technique is employed in calculating the required Green's functions. The complex images technique gives a closed-form Green's function, instead of evaluating time-consuming numerical integrals resulting in a fast and accurate Green's function calculation. Using singular value decomposition of the MDM at a frequency range of 1-3GHz and synthesized DORT method, images are constructed. Numerical results show the accuracy of the proposed approach in the detection and localization of buried objects. Furthermore, noise effect and sensitivity analysis on layers' permittivity values are studied. The results confirm the robustness of the method against noise and uncertain layers parameters.

A new design for the multilayer superlens, with the sub-wavelength imaging ability for various 2D objects in the visible range, is introduced and analyzed. The designed superlens will be more versatile for practical applications. A rigorous and efficient approach based on the method of moments is used to study the imaging performance of this structure. The imaging performance of the proposed superlens is evaluated using the correlation coefficient. In this work, the closed-form dyadic Green's functions in spatial domain, needed for the method of moments solutions, are obtained by applying the complex image method. Besides, the numerical integration is exploited to verify this method. The imaging results obtained via our approach are examined by comparison with the finite element method simulations that reveal good efficiency and accuracy of the proposed method. Keywords Complex image method . Green's functions . Integral equation method . Method of moments . Multilayer superlens * Keyvan Forooraghi

Three-dimensional (3D) particle localization at the nanometer scale plays a central role in 3D particle tracking and 3D localization-based super-resolution microscopy. Here we introduce a localization algorithm that is independent of theoretical models and therefore generally applicable to a large number of experimental realizations. Applying this algorithm and a convertible experimental setup we compare the performance of the two major 3D techniques based on astigmatic distortions and on multiplane detection. In both methods we obtain experimental 3D localization accuracies in agreement with theoretical predictions and characterize the depth dependence of the localization accuracy in detail.

Positron emission tomography images are used to diagnose, staggering, and monitoring several diseases like cancer and Alzheimer, also, this technique is used in clinical research to help to assess the therapeutic and toxic effects of drugs. However, a main drawback of this modality is the poor spatial resolution due to limiting factors such as positron range, instrumentation limits and the allowable doses of radiotracer for administration to patients. These factors also lead to low signal to noise ratios in the images. In this paper, we proposed to increment the resolution of the image and reduce noise by implementing a super resolution scheme, we proposed to use a hybrid regularization consisting of a TV term plus a Tikhonov term to solve the problem of low resolution and heavy noise. By using an anatomical driven scheme to balance between regularization terms we attain a better resolution image with preservation of small structures like lesions and reduced noise without blurring the edges of images. Experimental results and comparisons with other methods of the state-of-the-art show that our proposed scheme produces better preservation of details without adding artifacts when the resolution factor is increased.

Purpose Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents. Procedures The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI. Results We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % ...

A new concept of MMW imaging system is demonstrated in this study. The new concept is based on glow discharge detector (GDD) focal plane array (FPA) and a basic CMOS camera. The GDD elements emit optical light as a function of the incident MMW radiation intensity. By synchronizing the modulation of the MMW source with the exposure time of the CMOS camera in a way that every two consecutive frames were captured with the influence of the MMW radiation and without it, respectively, we were able to observe the effect of the MMW radiation alone on the GDD FPA without the light deriving from the GDD DC bias.

Aiming at the difficult problem of complex extraction for tree image in the existing complex background, we took tree species as the research object and proposed a fast recognition system solution for tree image based on Caffe platform and deep learning. In the research of deep learning algorithm based on Caffe framework, the improved Dual-Task CNN model (DCNN) is applied to train the image extractor and classifier to accomplish the dual tasks of image cleaning and tree classification. In addition, when compared with the traditional classification methods represented by Support Vector Machine (SVM) and Single-Task CNN model, Dual-Task CNN model demonstrates its superiority in classification performance. Then, in order for further improvement to the recognition accuracy for similar species, Gabor kernel was introduced to extract the features of frequency domain for images in different scales and directions, so as to enhance the texture features of leaf images and improve the recognition effect. The improved model was tested on the data sets of similar species. As demonstrated by the results, the improved deep Gabor convolutional neural network (GCNN) is advantageous in tree recognition and similar tree classification when compared with the Dual-Task CNN classification method. Finally, the recognition results of trees can be displayed on the application graphical interface as well. In the application graphical interface designed based on Ubantu system, it is capable to perform such functions as quick reading of and search for picture files, snapshot, one-key recognition, one-key exit and so on.

Impulsive noise filtering is an important problem of image processing. The problem of noise elimination is closely connected with the problem of maximal preservation of image edges. The requirement of maximal preservation of edges is especially important for images corrupted by impulsive noise with a low corruption rate. To avoid smoothing of the image during filtering, all noisy pixels must be detected. Then only these detected pixels must be corrected. We present in this paper two solutions to the edge preservation problem. The first one is an impulse detector. This detector is based on a comparison of signal samples within a narrow rank window. It is quite efficient for precise detection of impulses in images corrupted by impulsive noise with a low corruption rate. The second solution is based on threshold Boolean filtering, when the binary slices of an image, obtained by the threshold decomposition, are processed by original Boolean functions.

An image super-resolution is the development of creating a high-resolution image from low-resolution images for better image visualization for several recent applications. In this research paper, we propose DWT-based Image Super Resolution using Segmentation and Edge Detection for better picturing of images. The different kinds of colore images are considered and converted into greyscale images and resized to a uniform size of 166x304. The Discrete Wavelet Transform (DWT) is used to get one low-frequency and three high-frequency bands with a size of 83x152. The four bands are fused and dividing each coefficient by two to get Low Resolution (LR) images. Each coefficient of the LR image is inflated into 2x2 coefficients to get High Resolution (HR) image. The HR matrix is segmented into a 3x3 overlapped matrix and an average value is calculated and assigned to the whole HR matrix. The Canny edge detection is used on the original image to get the edge detected image. The pixel values of...

Fluorescence nanoscopy imaging permits the observation of periodic supramolecular protein structures in their natural environment, as well as the unveiling of previously unknown protein periodic structures. Deciphering the biological functions of such protein nanostructures requires systematic and quantitative analysis of large number of images under different experimental conditions and specific stimuli. Here we present a method and an open source software for the automated quantification of protein periodic structures in super-resolved images. Its performance is demonstrated by analyzing the abundance and regularity of the spectrin membrane-associated periodic skeleton (MPS) in hippocampal neurons of 2 to 40 days in vitro, imaged by STED and STORM nanoscopy. The automated analysis reveals that both the abundance and the regularity of the MPS increase over time and reach maximum plateau values after 14 DIV. A detailed analysis of the distributions of correlation coefficients provid...

Molybdenum was deposited at room temperature on the TiO 2 ͑110͒ surface in the 0-1.3 equivalent monolayer ͑eqML͒ range and was then annealed at 400 °C in order to reach a kind of equilibrium state. A threshold was found in the behavior of the deposit: below 0.2 eqML, substitutional molybdenum occurs in titanium sites located under the bridging oxygen atoms of the TiO 2 ͑110͒ surface. In this position, molybdenum atoms are in a structural and chemical MoO 2 -like environment. Density-functional theory calculations show that this molybdenum site is actually the most stable one in the case of isolated molybdenum atoms. Angle-scanned photoelectron diffraction data are in perfect agreement with such a hypothesis. For deposits higher than 0.2 eqML, the increased amount of molybdenum atoms raises the probability of Mo-Mo interactions during the annealing at 400 °C, taking to a reduction process of the deposit. However, such annealing does not allow the deposit to become fully metallic: molybdenum clusters formed during the annealing are in strong interaction with the substrate, and metallic molybdenum can be obtained only depositing a film thicker than 1 ML.

We address the problem of super resolved generation of novel views of a 3D scene with the reference images obtained from cameras in general positions; a problem which has not been tackled before in the context of super resolution and is also of importance to the field of image based rendering. We formulate the problem as one of estimation of the color at each pixel in the high resolution novel view without explicit and accurate depth recovery. We employ a reconstruction based approach using MRF-MAP formalism and solve using graph cut optimization. We also give an effective method to handle occlusion. We present compelling results on real images.

Text-to-image era, driven by later propels in huge dialect models (LLMs), has quickly advanced from simple pixel blend to the era of high-resolution, relevantly exact pictures from characteristic dialect prompts. This paper presents a comprehensive survey of the basic standards, engineering components, applications, and experimental comes about related to LLM-driven text-to-image frameworks. We look at the integration of transformers with generative ill-disposed systems (GANs)[1], dissemination models[2], and multi-modal encoders such as CLIP (Contrastive Language Image Pretraining)[3] , nearby more current systems like DALLE[6]and Midjourney.

Recognition and identification ranges are limited to the quality of the images. Both the received contrast and the spatial resolution determine if objects are recognizable. Several aspects affect the image quality. First of all the sensor itself. The image quality depends on the size of the infrared detector array and the sensitivity. Second, also the intervening atmosphere, in particular over longer ranges, has an impact on the image quality. It degrades the contrast, due to transmission effects, as well as it influences the resolution, due to turbulence blur, of the image. We present studies in the field of infrared image enhancement. Several techniques are described: noise reduction, super resolution, turbulence compensation, contrast enhancement, stabilization. These techniques operate in real-time on COTS/MOTS platforms. They are especially effective in the army theatre, where long horizontal paths, and short line-of-sight limited urban operations are both present. Application of these techniques on observation masts, such as on military camp sites, and on UAVs and moving ground vehicles are discussed. Examples will be presented from several trials in which these techniques were demonstrated, including the presentation of test results.

The current state of the art in electro-optics provides systems with high image quality for associated prices and less expensive systems with subsequent lower performance. This keynote will expound how image processing enables to obtain high quality imagery while utilizing affordable system components. Furthermore, where conventional high-end EO systems are hindered by too much clutter and badly require often unavailable operator assistance, it will be shown that image processing can autonomously generate that information as needed in today's missions. Noise reduction techniques as implemented by image processing can bring uncooled thermal imagers up to cooled IR system performance levels. Turbulence mitigation techniques can provide long-range surveillance capabilities beyond what is achievable using optical techniques only. Super resolution and image stitching techniques can provide high resolution imagery using modest imagers. In cluttered areas autonomous object detection techniques are essential to dedicate operator attention to the most significant events where the system copes with the complex environment. IED detection is only effective with clutter reduction techniques such as change detection or shape matching; small sea targets filtered from sea-state and coastal backgrounds; subjects shall be discovered with motion-in-motion techniques when manoeuvring in urban areas. As image processing is a key element to provide high-quality information for affordable prices, it is the enabler for many-sensor systems, including distributed sensing on single vehicles, and integrated views of all group members.

We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction.

Hyperspectral imaging is a continuously growing area of remote sensing application. The wide spectral range, providing a very high spectral resolution, allows to detect and classify surfaces and chemical elements of the observed image. The main problem of hyperspectral data is that the high spectral resolution is usually complementary to the spatial one, which can vary from a few to tens of meters. Many factors, such as imperfect imaging optics, atmospheric scattering, secondary illumination effects and sensor noise cause a degradation of the acquired image quality, making the spatial resolution one of the most expensive and hardest to improve in imaging systems. In this work, a novel method, based on the use of source separation technique and a spatial regularization step by simulated annealing is proposed to improve the spatial resolution of cover classification maps. Experiments have been carried out on both synthetic and real hyperspectral data and show the effectiveness of the proposed method.

Stimulation emission depletion (STED) microscopy breaks the spatial resolution limit of conventional light microscopy while retaining its major advantages, such as working under physiological conditions. These properties make STED microscopy a perfect tool for investigating dynamic sub-cellular processes in living organisms. However, up to now, the massive dissemination of STED microscopy has been hindered by the complexity and cost of its implementation. Gated CW-STED (gCW-STED) substantially helps solve this problem without sacrificing spatial resolution. Here, we describe a versatile gCW-STED microscope able to speedily image the specimen, at a resolution below 50 nm, with light intensities comparable to the more complicated all-pulsed STED system. We show this ability on calibration samples as well as on biological samples.

Erythrocyte invasion by the merozoite is an obligatory stage in Plasmodium parasite infection and essential to malaria disease progression. Attempts to study this process have been hindered by the poor invasion synchrony of merozoites from the only in vitro culture-adapted human malaria parasite, Plasmodium falciparum. Using fluorescence, three-dimensional structured illumination, and immunoelectron microscopy of filtered merozoites, we analyze cellular and molecular events underlying each discrete step of invasion. Monitoring the dynamics of these events revealed that commitment to the process is mediated through merozoite attachment to the erythrocyte, triggering all subsequent invasion events, which then proceed without obvious checkpoints. Instead, coordination of the invasion process involves formation of the merozoite-erythrocyte tight junction, which acts as a nexus for rhoptry secretion, surface-protein shedding, and actomyosin motor activation. The ability to break down each molecular step allows us to propose a comprehensive model for the molecular basis of parasite invasion.