Deep Physics-Guided Unrolling Generalization for Compressed Sensing-Reference-Cited by-同舟云学术

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Deep Physics-Guided Unrolling Generalization for Compressed Sensing

Published:2023-07-04 Issue:11 Volume:131 Page:2864-2887
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Chen Bin^ORCID,Song Jiechong^ORCID,Xie Jingfen^ORCID,Zhang Jian^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s11263-023-01814-w.pdf

Reference94 articles.

1. Adler, J., & Öktem, O. (2018). Learned primal-dual reconstruction. IEEE Transactions on Medical Imaging, 37(6), 1322–1332.

2. Agustsson, E., & Timofte, R. (2017). NTIRE 2017 challenge on single image super-resolution: dataset and study. In Proceedings of IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp. 126–135.

3. Björck, Å., Elfving, T., & Strakos, Z. (1998). Stability of Conjugate Gradient and lanczos methods for linear least squares problems. SIAM Journal on Matrix Analysis and Applications, 19(3), 720–736.

4. Blumensath, T., & Davies, M. E. (2009). Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis, 27(3), 265–274.

5. Boufounos, Petros. T., & Baraniuk, Richard. G. (2008). 1-bit Compressive sensing. In Proceedings of IEEE conference on information sciences and systems (CISS), pp. 16–21.

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