Type
ArticleAuthors
Zhang, JianZhao, Chen
Gao, Wen
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionVisual Computing Center (VCC)
Date
2020-03-02Online Publication Date
2020-03-02Print Publication Date
2020-05Permanent link to this record
http://hdl.handle.net/10754/661850
Metadata
Show full item recordAbstract
In order to improve CS performance of natural images, in this paper, we propose a novel framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE-Net, for adaptive sampling and recovery. Both orthogonal and binary constraints of sampling matrix are incorporated into OPINE-Net simultaneously. In particular, OPINE-Net is composed of three subnets: sampling subnet, initialization subnet and recovery subnet, and all the parameters in OPINE-Net (\eg sampling matrix, nonlinear transforms, shrinkage threshold, step size) are learned end-to-end, rather than hand-crafted. Moreover, considering the relationship among neighboring blocks, an enhanced version OPINE-${\rm{Net^{+}}}$ is developed, which allows image blocks to be sampled independently but reconstructed jointly to further enhance the performance. In addition, some interesting findings of learned sampling matrix are presented. Compared with existing state-of-the-art network-based CS methods, the proposed hardware-friendly OPINE-Nets not only achieve better performance but also require much fewer parameters and much less storage space, while maintaining a real-time computational speed.Citation
Zhang, J., Zhao, C., & Gao, W. (2020). Optimization-Inspired Compact Deep Compressive Sensing. IEEE Journal of Selected Topics in Signal Processing, 14(4), 765–774. doi:10.1109/jstsp.2020.2977507Additional Links
https://ieeexplore.ieee.org/document/9019857/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9019857
ae974a485f413a2113503eed53cd6c53
10.1109/JSTSP.2020.2977507