| Title | DR2-Net: Deep Residual Reconstruction Network for image compressive sensing |
| Authors | Yao, Hantao Dai, Feng Zhang, Shiliang Zhang, Yongdong Tian, Qi Xu, Changsheng |
| Affiliation | Inst Automat CAS, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China Inst Comp Technol CAS, Beijing 100190, Peoples R China Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China Univ Sci & Technol China, Natl Engn Lab Brain Inspired Intelligence Technol, Hefei 230026, Anhui, Peoples R China Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| Keywords | Image compressive sensing DR2-Net Convolutional neural networks |
| Issue Date | 2019 |
| Publisher | NEUROCOMPUTING |
| Abstract | Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed (CS) measurement. The DR2-Net is proposed based on two observations: (1) linear mapping could reconstruct a high-quality preliminary image, and (2) residual learning could further improve the reconstruction quality. Accordingly, DR2-Net consists of two components, i.e., linear mapping network and residual network, respectively. Specifically, the fully-connected layer in neural network implements the linear mapping network. We then expand the linear mapping network to DR2-Net by adding several residual learning blocks to enhance the preliminary image. Extensive experiments demonstrate that the DR2 -Net outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively. The code of DR 2 -Net has been released on: https://github.com/coldrainyht/caffe_dr2. (C) 2019 Elsevier B.V. All rights reserved. |
| URI | http://hdl.handle.net/20.500.11897/545050 |
| ISSN | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.05.006 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 信息科学技术学院 |
