Name
Abstract | ||
|---|---|---|
Deep learning has been widely adopted in compression sensing (CS) to achieve superior reconstruction quality, but is restricted by the black-box architecture in network design and lack of interpretability. In this paper, we propose a novel deep network-based CS framework via unfolding the $\ell_{0}$-constrained convolutional sparse coding (CSC). The proposed method incorporates deep neural network... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DCC50243.2021.00026 | 2021 Data Compression Conference (DCC) |
Keywords | DocType | ISSN |
Convolutional codes,Deep learning,Image coding,Magnetic resonance imaging,Neural networks,Data compression,Encoding | Conference | 1068-0314 |
ISBN | Citations | PageRank |
978-1-6654-0333-7 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaqi Sun | 1 | 0 | 0.34 |
| Wenrui Dai | 2 | 64 | 25.01 |
| Chenglin Li | 3 | 116 | 17.93 |
| J. Zou | 4 | 203 | 35.51 |
| Hongkai Xiong | 5 | 22 | 8.85 |