128 citations · 135 across the 4 of their papers we have counts for
6 papers
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding
Vardan Papyan, Jeremias Sulam, Michael Elad
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…
Convolutional Dictionary Learning via Local Processing
Vardan Papyan, Yaniv Romano, Jeremias Sulam +1
Convolutional Sparse Coding (CSC) is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based spars…
On the Global-Local Dichotomy in Sparsity Modeling
Dmitry Batenkov, Yaniv Romano, Michael Elad
The traditional sparse modeling approach, when applied to inverse problems with large data such as images, essentially assumes a sparse model for small overlapping data patches. Wh…
A Deep Learning Approach to Block-based Compressed Sensing of Images
Amir Adler, David Boublil, Michael Elad +1
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.…
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution
Wen-Ze Shao, Michael Elad
This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with…
Can we allow linear dependencies in the dictionary in the sparse synthesis framework?
Raja Giryes, Michael Elad
Signal recovery from a given set of linear measurements using a sparsity prior has been a major subject of research in recent years. In this model, the signal is assumed to have a…