Region-based weighted-norm with adaptive regularization for resolution enhancement
Section snippets
Osama A. Omer received his B.Eng. and M.Eng. degrees in electrical engineering from South Valley University, Aswan, Egypt, in 2000 and 2004, respectively. He received his Ph.D. degree from Tokyo University of Agriculture and Technology in 2009. He is now an Assistant Professor at South Valley University. He spent the summer internship 2008 in Nokia/Tokyo research center. His interests include image/video super-resolution, image/video compression, and applications of neural networks in signal
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Multiscale self-similarity and sparse representation based single image super-resolution
2017, NeurocomputingCitation Excerpt :The prominent advantage of TV is that it tends to have a better edge preservation than some other regularization model, such as Tikhonov regularization [17], but it is quite liable to produce some unpleasing pseudo-edges in the smooth regions, especially at high noise levels. Thus, a variety of derivatives of TV are proposed to improve its performance from some particular aspects, e.g., bilateral TV [18], adaptive TV [19], locally adaptive BTV [20–23], etc. However, it has been pointed out that the performance of this kind of methods all degrades dramatically under three circumstances where (a) the amount of LR inputs is inadequate; (b) the estimate of motion is imprecise; or (c) the scale factor increases [24,25].
A new denoising model for multi-frame super-resolution image reconstruction
2017, Signal ProcessingCitation Excerpt :After the first work proposed in [19], where the authors considered a frequency domain approach, several approaches have been proposed and studied to improve the multi-frame SR problem [20–24]. Earlier works on SR algorithms are based on regularization method due to its ill-posed nature which mainly contains the likelihood and prior function [25,26]. The likelihood function measures the difference between the LR images and the obtained HR one, while the image prior function, impose some prior knowledge on the desired HR image.
Single-image super-resolution reconstruction based on global non-zero gradient penalty and non-local Laplacian sparse coding
2014, Digital Signal Processing: A Review JournalCitation Excerpt :Therefore, the signal processing methods are selected to reconstruct potential details and features hidden in the low resolution (LR) image. Generally, the existing methods can be classified into three categories: interpolation-based methods [2–5], regularization-based methods [6–12] and example-based methods [13–32]. However, the interpolation-based methods are usually prone to yield overly smooth images with ringing and jagged artifacts when a larger magnification ratio (such as a factor of more than double) is performed.
A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization
2013, Digital Signal Processing: A Review Journal
Osama A. Omer received his B.Eng. and M.Eng. degrees in electrical engineering from South Valley University, Aswan, Egypt, in 2000 and 2004, respectively. He received his Ph.D. degree from Tokyo University of Agriculture and Technology in 2009. He is now an Assistant Professor at South Valley University. He spent the summer internship 2008 in Nokia/Tokyo research center. His interests include image/video super-resolution, image/video compression, and applications of neural networks in signal processing.
Toshihisa Tanaka received the B.E., the M.E., and the Ph.D. degrees from the Tokyo Institute of Technology in 1997, 2000, and 2002, respectively. From 2000 to 2002, he was a JSPS Research Fellow. From October 2002 to March 2004, he was a Research Scientist at RIKEN Brain Science Institute. In April 2004, he joined Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, where he is currently an Associate Professor. His research interests include image and signal processing, multirate systems, blind signal separation, brain signal processing, and adaptive signal processing. In 2005, he was a Royal Society Visiting Fellow at the Communications and Signal Processing Group, Imperial College London, U.K. He is a co-editor of Signal Processing Techniques for Knowledge Extraction and Information Fusion (with Mandic, Splinger), 2008. He has been a member of the Technical Committee on Blind Signal Processing, IEEE Circuits and Systems Society. He is a chair of the Technical Committee on Biomedical Signal Processing, APSIPA. He is a senior member of IEEE, and a member of IEICE and APSIPA.