Resolution and its Enhancement in Imaging

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

Super-resolution is a technique that produces higher resolution images from low resolution images (LRIs). In practice, people have found that the improvement in resolution is limited. The aim of this paper is to address the problem "do fundamental limits exist for super-resolution?". Specifically, this paper provides explicit limits for a major class of super-resolution algorithms, called the reconstructionbased algorithms, under both real and synthetic conditions. Our analysis is based on perturbation theory of linear systems. We also show that a sufficient number of LRIs can be determined to reach the limit. Both real and synthetic experiments are carried out to verify our analysis.

High resolution (HR) images have great importance in many areas, such as astronomy, medical, video surveillance, etc. These HR images give some additional details which are of great significance for analysis in various applications. This paper investigates mainly on various methods of super resolution that are existing, and putting it all together for a literature survey. Scope of this study mainly focuses on the different available techniques of image processing to get high resolution images.

IEEE Transactions on Image Processing, 2000

Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of lowquality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Cramér-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration.

In applications such as super-resolution imaging and mosaicking, multiple video sequences are registered to reconstruct video with enhanced resolution. However, not all computed registration is reliable. In addition, not all sequences contribute useful information towards reconstruction from multiple non-uniformly distributed sample sets. In this paper we present two algorithms that can help determine which low resolution sample sets should be combined in order to maximize reconstruction accuracy while minimizing the number of sample sets. The first algorithm computes a confidence measure which is derived as a combination of two objective functions. The second algorithm is an iterative ranked-based method for reconstruction which uses confidence measures to assign priority to sample sets that maximize information gain while minimizing reconstruction error. Experimental results with real and synthetic sequences validate the effectiveness of the proposed algorithms. Application of our work in medical visualization and super-resolution reconstruction of MRI data are also presented.

JISOM, 2014

Although the technology of optical instruments is constantly advancing, the capture of high resolution images is limited by both the shortcoming s of the imaging devices and the law of physics (uncertainty principle applied onto photons or the wave like theory of light). The current paper presents an algorithm for processing a set of images sharing the same subject with the purpose of extracting a higher resolution output image of the subject, using partial information from every one of the low resolutions samples in the input set.

2013

This paper studies the recovery of a superposition of point sources from noisy bandlimited data. In the fewest possible words, we only have information about the spectrum of an object in the lowfrequency band [−f lo , f lo ] and seek to obtain a higher resolution estimate by extrapolating the spectrum up to a frequency f hi > f lo. We show that as long as the sources are separated by 2/f lo , solving a simple convex program produces a stable estimate in the sense that the approximation error between the higher-resolution reconstruction and the truth is proportional to the noise level times the square of the super-resolution factor (SRF) f hi /f lo .

Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution imaging (SR) is a class of techniques that enhance the resolution of an imaging system. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy.

IEEE Signal Processing Magazine, 2003

Stochastic regularized methods are quite advantageous in super-resolution (SR) image reconstruction problems. In the particular techniques, the SR problem is formulated by means of two terms, the datafidelity term and the regularization term. The present work examines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the low-resolution (LR) frames. Experimentation is carried out with the widely employed L 2 , L 1 , Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effective SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. Otherwise, experimental conclusions regarding performance ranking vary with the presence of noise in frames, the noise model as well as the difference in robustness of efficiency between the rival terms. Estimators employed for the data-fidelity term or regularizations stand for the rival terms.

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013

This paper presents a new reconstruction operator to be used in a super-resolution scheme. Here, by reconstruction in super-resolution, we mean the back-projection operation, i.e. the way K low resolution (LR) images are aggregated to obtain a smooth high resolution (HR) image. Within this method, we replace the usual reconstruction procedure by a non-additive reconstruction operation based on the nice properties of fuzzy partitions. This non-additive reconstruction operator represents a convex family of usual additive reconstruction operators. The obtained reconstructed image is thus a convex family of usual reconstructed images. It allows the super-resolution method to be less sensitive to the choice of the reconstruction method. To make the reading of this method easier, it is presented with 1D signals. We present some experiments to illustrate the proved properties of this new operator.

Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings., 2002

Image sensing is usually done with multiple sensors, like the RGB sensors in color imaging, the IR and EO sensors in surveillance and satellite imaging, etc. The resolution of each sensor can be increased by considering the images of the other sensors, and using the statistical redundancy among the sensors. Particularly, we use the fact that most discontinuities in the image of one sensor correspond to discontinuities in the other sensors. Two applications are presented: Increasing the resolution of a single color image by using the correlation among the three color channels, and enhancing noisy IR images.