A Deep Network Based on Wavelet Transform for Image Compressed Sensing
文献类型：期刊论文
| 作者 | Yin, Zhu1,2; Wu, Zhongcheng1,2 ; Zhang, Jun1
|
| 刊名 | CIRCUITS SYSTEMS AND SIGNAL PROCESSING
 |
| 出版日期 | 2022-06-06 |
| 关键词 | Compressed sensing Sparse representation Sampling network Multi-scale residual Reconstruction network |
| ISSN号 | 0278-081X |
| DOI | 10.1007/s00034-022-02058-8 |
| 通讯作者 | Yin, Zhu(yinzhu@mail.ustc.edu.cn) |
| 英文摘要 | Most conventional compressed sensing (CS) algorithms are impaired by the fact that the optimization of image reconstruction suffers from the need for multiple iterative calculations. Recently, deep learning-based CS algorithms have been proposed and they dramatically achieve efficient reconstruction and fast computing speed with fewer sampling measurements than traditional iterative optimization-based algorithms. However, the sampling process of common deep learning-based CS and traditional CS generally cannot sufficiently exploit the structural sparsity of image sequences to effectively conduct CS research. Motivated by the fact that a sparser signal is easier to reconstruct accurately, in this paper, we propose two novel algorithms called the WCS-Nets (WCS-Net and WCS-Net(+)), which synthesize the advantages of a sampling network based on sparse representation and a deep elastic reconstruction network. WCS-Net is an improvement in DR2-Net, and its primary innovation focuses on combining the sym8 wavelet transform with a sampling network. Moreover, considering that multi-scale residual learning has better reconstruction efficiency, an enhanced version, called WCS-Net(+), is designed in the deep elastic reconstruction network and further improves the reconstruction accuracy. Experimental results demonstrate that the proposed methods achieve better results when compared with other state-of-the-art deep learning-based and traditional CS algorithms in terms of reconstruction quality, runtime and robustness to noise. |
| WOS关键词 | RECONSTRUCTION ; RECOVERY |
| 资助项目 | Key Research and Development Project of Hefei University Science Center, Chinese Academy of Sciences |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000806674000002 |
| 出版者 | SPRINGER BIRKHAUSER |
| 资助机构 | Key Research and Development Project of Hefei University Science Center, Chinese Academy of Sciences |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/131195]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Yin, Zhu |
| 作者单位 | 1.Chinese Acad Sci, Hefei Inst Phys Sci, High Magnet Field Lab, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Zhu,Wu, Zhongcheng,Zhang, Jun. A Deep Network Based on Wavelet Transform for Image Compressed Sensing[J]. CIRCUITS SYSTEMS AND SIGNAL PROCESSING,2022. |
| APA | Yin, Zhu,Wu, Zhongcheng,&Zhang, Jun.(2022).A Deep Network Based on Wavelet Transform for Image Compressed Sensing.CIRCUITS SYSTEMS AND SIGNAL PROCESSING. |
| MLA | Yin, Zhu,et al."A Deep Network Based on Wavelet Transform for Image Compressed Sensing".CIRCUITS SYSTEMS AND SIGNAL PROCESSING (2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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