Advances and Challenges in Super-Resolution

Abstract

Super-Resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This article reviews a variety of Super-Resolution methods proposed in the last 20 years, and provides some insight into, and a summary of, our recent contributions to the general Super-Resolution problem. In the process, a detailed study of several very important aspects of Super-Resolution, often ignored in the literature, is presented. Specifically, we discuss robustness, treatment of color, and dynamic operation modes. Novel methods for addressing these issues are accompanied by experimental results on simulated and real data. Finally, some future challenges in Super-Resolution are outlined and discussed.

Resolution enhancement by image fusion for microgrid polarization imagers

“…In [9], Farsiu et al note that color super resolution approaches that restore each color channel separately or that restore luminance but not chrominance are sub-optimal because they do not exploit all of the information that is shared between bands. In the same way, individual polarization channels also share information that should be used to improve the SR estimator.…”

Super-Resplution Procedures in Images and Video Sequences Based on Wavelet Atomic Functions

“…The temporal resolution is determined by the frame rate and the exposure time, which limits the maximum speed that can be observed correctly in video. Because of the physical limitations and high cost needed to improve the precision and stability of the imaging system by manufacturing techniques, many applications of image and video sequence data (Farsiu et al, 2004), such as those mentioned above, demand to develop additional methods and algorithms that should restore the resolution degraded in a sensor permitting better observations of the fine details, edges, and restoration of the colour properties. Superresolution (SR) is defined as a reconstruction of a high-resolution (HR) image or frame in the video sequence from one or multiple low-resolution (LR) images/videos, which is relatively inexpensive to implement.…”

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Bayesian Approach with Lorentzian Norm

“…The super-resolution reconstruction problem is well known and extensively treated in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The basic ideal behind (SRR) Super-Resolution…”

“…M. Elad and A. Feuer [7] proposed the fast SRR algorithm ML estimator (L2 Norm) for restoration the warps are pure translations, the blur is space invariant and the same for all the images, and the noise is i. [15][16] proposed SRR algorithm ML estimator (LI Norm) with BTV Regularization in 2004. Next, they propose a fast SRR of color images [17] using ML estimator (LI Norm) with BTV and Tikhonov Regularization in 2006.…”

“…[15][16] proposed SRR algorithm ML estimator (LI Norm) with BTV Regularization in 2004. Next, they propose a fast SRR of color images [17] using ML estimator (LI Norm) with BTV and Tikhonov Regularization in 2006.…”