Multilevel wavelet-based hierarchical networks for image compressed sensing
文献类型：期刊论文
| 作者 | Yin, Zhu1,2; Shi, WuZhen3; Wu, Zhongcheng1,2 ; Zhang, Jun2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 129 |
| 关键词 | Compressed sensing Hierarchical reconstruction Sparse signal Multilevel wavelet transform |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2022.108758 |
| 通讯作者 | Yin, Zhu(yinzhu@mail.ustc.edu.cn) |
| 英文摘要 | Recently, deep learning-based compressed sensing (CS) algorithms have been reported, which remarkably achieve pleasing reconstruction quality with low computational complexity. However, the sampling process of the common deep learning-based CS methods and the conventional ones cannot sufficiently exploit the structured sparsity within image sequences, especially in preserving finer texture details. In this paper, we propose a novel multilevel wavelet-based hierarchical networks for image compressed sensing (dubbed MWHCS-Net). In particular, MWHCS-Net consists of three modules: a sampling module based on a multilevel wavelet transform, a hierarchical initial reconstruction module and a lightweight deep reconstruction module. Motivated by the fact that a sparser signal is easier to reconstruct accurately, we present the sampling module based on multilevel wavelet transform with hierarchical subspace learning for progressive acquisition of measurements to further optimize sampling efficiency and stability. To enhance the finer texture details, the hierarchical initial reconstruction module is designed as a basic initial reconstruction network plus an enhanced initial reconstruction network, which corresponding to the dominant structure component and the texture detail component of the reconstructed image, respectively. At the same time, we also further explore the impact of the hierarchical initial reconstruction module and prove that the texture detail component branch plays an important role in improving the reconstruction quality. Experimental results demonstrate that the proposed MWHCS-Net achieves the state-of-the-art performance while maintaining an efficient running speed. Furthermore, MWHCS-Net outperforms the existing image CS methods based on deep learning in terms of anti-noise performance in most cases.(c) 2022 Elsevier Ltd. All rights reserved. |
| WOS关键词 | RECONSTRUCTION ; DECOMPOSITION |
| 资助项目 | Research on Scientific Data Management Method and Key Technology of Large-scale Scientific Facility, Key Program of Research and Development of Hefei Science Center, CAS[2019HSC-KPRD003] ; High Magnetic Field Laboratory of Anhui Province ; Suzhou University Scientific Research Platform Project[2019ykf31] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000805703300007 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | Research on Scientific Data Management Method and Key Technology of Large-scale Scientific Facility, Key Program of Research and Development of Hefei Science Center, CAS ; High Magnetic Field Laboratory of Anhui Province ; Suzhou University Scientific Research Platform Project |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/131199]  |
| 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.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, High Magnet Field Lab, Hefei 230031, Peoples R China 3.Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen 518060, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Zhu,Shi, WuZhen,Wu, Zhongcheng,et al. Multilevel wavelet-based hierarchical networks for image compressed sensing[J]. PATTERN RECOGNITION,2022,129. |
| APA | Yin, Zhu,Shi, WuZhen,Wu, Zhongcheng,&Zhang, Jun.(2022).Multilevel wavelet-based hierarchical networks for image compressed sensing.PATTERN RECOGNITION,129. |
| MLA | Yin, Zhu,et al."Multilevel wavelet-based hierarchical networks for image compressed sensing".PATTERN RECOGNITION 129(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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