Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414163 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Compressed sensing, block-sparse, deep unfolding, learned ISTA, Adaptive-LISTA, harmonic retrieval problem | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Fu | 1 | 33 | 5.56 |
| Vincent Monardo | 2 | 0 | 1.01 |
| Tianyao Huang | 3 | 79 | 10.86 |
| Yimin Liu | 4 | 158 | 25.46 |