Deep Unfolding Network for Block-Sparse Signal Recovery
Rong Fu, Vincent Monardo, Tianyao Huang, Yimin Liu
Abstract
Block-sparse signal recovery has drawn increasing attention in many areas of signal processing, where the goal is to recover a high-dimensional signal whose non-zero coefficients only arise in a few blocks from compressive measurements. However, most off-the-shelf data-driven reconstruction networks do not exploit the block-sparse structure. Thus, they suffer from deteriorating performance in block-sparse signal recovery. In this paper, we put forward a block-sparse reconstruction network named Ada-BlockLISTA based on the concept of deep unfolding. Our proposed network consists of a gradient descent step on every single block followed by a block-wise shrinkage step. We evaluate the performance of the proposed Ada-BlockLISTA network through simulations based on the signal model of two-dimensional (2D) harmonic retrieval problems.
BibTeX
@inproceedings{icassp2021_deepunfoldingnet,
title = {Deep Unfolding Network for Block-Sparse Signal Recovery},
author = {Rong Fu and Vincent Monardo and Tianyao Huang and Yimin Liu},
booktitle = {ICASSP 2021},
year = {2021}
}