dc.contributor.author Zhang, Jian dc.contributor.author Zhao, Chen dc.contributor.author Gao, Wen dc.date.accessioned 2020-03-03T05:41:43Z dc.date.available 2020-03-03T05:41:43Z dc.date.issued 2020-03-02 dc.identifier.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.2020.2977507 dc.identifier.doi 10.1109/JSTSP.2020.2977507 dc.identifier.uri http://hdl.handle.net/10754/661850 dc.description.abstract 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. dc.publisher Institute of Electrical and Electronics Engineers (IEEE) dc.relation.url https://ieeexplore.ieee.org/document/9019857/ dc.relation.url https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9019857 dc.rights Archived with thanks to IEEE Journal of Selected Topics in Signal Processing dc.title Optimization-Inspired Compact Deep Compressive Sensing dc.type Article dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Visual Computing Center (VCC) dc.identifier.journal IEEE Journal of Selected Topics in Signal Processing dc.eprint.version Pre-print dc.contributor.institution School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, 429362 Nanshan, Shenzhen China dc.contributor.institution Peking University School of Electronics Engineering and Computer Science, 529005 Beijing China kaust.person Zhao, Chen refterms.dateFOA 2020-03-03T05:42:51Z dc.date.published-online 2020-03-02 dc.date.published-print 2020-05
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