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20132022
most citedWorking Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding

128 citations · 155 across the 10 of their papers we have counts for

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cs.CV2022

What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems

Gilad Kutiel, Regev Cohen, Michael Elad +1

Estimating uncertainty in image-to-image networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms.…

cs.CV20222 cited

Patch-Craft Self-Supervised Training for Correlated Image Denoising

Gregory Vaksman, Michael Elad

Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images…

cs.CV2021

Patch Craft: Video Denoising by Deep Modeling and Patch Matching

Gregory Vaksman, Michael Elad, Peyman Milanfar

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…

cs.CV2019

DeepRED: Deep Image Prior Powered by RED

Gary Mataev, Michael Elad, Peyman Milanfar

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immens…

cs.CV2018

Unsupervised Single Image Dehazing Using Dark Channel Prior Loss

Alona Golts, Daniel Freedman, Michael Elad

Single image dehazing is a critical stage in many modern-day autonomous vision applications. Early prior-based methods often involved a time-consuming minimization of a hand-crafte…

cs.CV2018

A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model

Ev Zisselman, Jeremias Sulam, Michael Elad

The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities. By providing a global, yet tractable, model th…