V Papyan, M Elad - IEEE Transactions on image processing, 2015 - ieeexplore.ieee.org
Many image restoration algorithms in recent years are based on patch processing. The core idea is to decompose the target image into fully overlapping patches, restore each of them …
This work considers noise removal from images, focusing on the well-known K-SVD denoising algorithm. This sparsity-based method was proposed in 2006, and for a short …
This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for …
The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing …
D Simon, M Elad - Advances in neural Information …, 2019 - proceedings.neurips.cc
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing. In recent years, the …
V Papyan, J Sulam, M Elad - IEEE Transactions on Signal …, 2017 - ieeexplore.ieee.org
The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a …
Y Romano, M Elad - SIAM Journal on Imaging Sciences, 2015 - SIAM
In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following “SOS” …
We propose a fully convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising …
A Brifman, Y Romano, M Elad - 2016 IEEE International …, 2016 - ieeexplore.ieee.org
Denoising and Super-Resolution are two inverse problems that have been extensively studied. Over the years, these two tasks were treated as two distinct problems that deserve a …