Name
Abstract | ||
|---|---|---|
Compressed sensing (CS) can recover an image from a few random measurements by exploiting the sparsity assumption on the structure of images. Some recent generative model-based CS recovery methods have removed the sparsity constraint, but their recovery process is slow and the recovered signal is constrained to be in the generator range. Here, we propose a new framework, called Proximal-Gen, for CS recovery. Specifically, we first formulate a general domain of the recovered signals, this allows the subsequent recovery algorithms to recover the signals that deviate from the generator range. Then based on the general domain, we develop a fast recovery algorithm, which mainly consists of two sub-algorithms, namely network-based projected gradient descent (NPGD) and denoiser-based proximal gradient descent (DPGD). The NPGD is used to obtain an intermediate signal lying in the generator range, while the DPGD is proposed to recover a deviation signal. Compared with multiple recent generative model-based recovery methods, our method can achieve better reconstruction performance and higher efficiency under most measurements. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jvcir.2021.103358 | Journal of Visual Communication and Image Representation |
Keywords | DocType | Volume |
Compressed sensing,Generative models,Generator range,Reconstruction efficiency | Journal | 82 |
ISSN | Citations | PageRank |
1047-3203 | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Cai | 1 | 0 | 1.01 |
| Yuli Fu | 2 | 0 | 0.34 |
| Tao Zhu | 3 | 0 | 2.37 |
| Youjun Xiang | 4 | 0 | 0.34 |
| Huanqiang Zeng | 5 | 0 | 0.34 |