128 citations · 155 across the 10 of their papers we have counts for
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Stochastic Image Denoising by Sampling from the Posterior Distribution
Bahjat Kawar, Gregory Vaksman, Michael Elad
Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image. Unfortunatel…
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study
Xiyang Luo, Hossein Talebi, Feng Yang +2
Handling digital images is almost always accompanied by a lossy compression in order to facilitate efficient transmission and storage. This introduces an unavoidable tension betwee…
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Regev Cohen, Michael Elad, Peyman Milanfar
Inverse problems in image processing are typically cast as optimization tasks, consisting of data-fidelity and stabilizing regularization terms. A recent regularization strategy of…
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation
Gregory Vaksman, Michael Elad, Peyman Milanfar
Image denoising is a well studied problem with an extensive activity that has spread over several decades. Despite the many available denoising algorithms, the quest for simple, po…
Rethinking the CSC Model for Natural Images
Dror Simon, Michael Elad
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing. In recent years, the Convolutional…
Unified Single-Image and Video Super-Resolution via Denoising Algorithms
Alon Brifman, Yaniv Romano, Michael Elad
Single Image Super-Resolution (SISR) aims to recover a high-resolution image from a given low-resolution version of it. Video Super Resolution (VSR) targets series of given images,…