LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing
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LR-CSNet : Low-Rank Deep Unfolding Network for Image Compressive Sensing. / Zhang, Tianfang; Li, Lei; Igel, Christian; Oehmcke, Stefan; Gieseke, Fabian; Peng, Zhenming.
2022 IEEE International Conference on Computer and Communications (ICCC), Chengdu, China. IEEE, 2023. p. 1951-1957.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - LR-CSNet
T2 - International Conference on Computer and Communications
AU - Zhang, Tianfang
AU - Li, Lei
AU - Igel, Christian
AU - Oehmcke, Stefan
AU - Gieseke, Fabian
AU - Peng, Zhenming
N1 - Conference code: 8
PY - 2023
Y1 - 2023
N2 - Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS. Real-world image patches are often well-represented by low-rank approximations. LR-CSNet exploits this property by adding a low-rank prior to the CS optimization task. We derive a corresponding iterative optimization procedure using variable splitting, which is then translated to a new DUN architecture. The architecture uses low-rank generation modules (LRGMs), which learn low-rank matrix factorizations, as well as gradient descent and proximal mappings (GDPMs), which are proposed to extract high-frequency features to refine image details. In addition, the deep features generated at each reconstruction stage in the DUN are transferred between stages to boost the performance. Our extensive experiments on three widely considered datasets demonstrate the promising performance of LR-CSNet compared to state-of-the-art methods in natural image CS.
AB - Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS. Real-world image patches are often well-represented by low-rank approximations. LR-CSNet exploits this property by adding a low-rank prior to the CS optimization task. We derive a corresponding iterative optimization procedure using variable splitting, which is then translated to a new DUN architecture. The architecture uses low-rank generation modules (LRGMs), which learn low-rank matrix factorizations, as well as gradient descent and proximal mappings (GDPMs), which are proposed to extract high-frequency features to refine image details. In addition, the deep features generated at each reconstruction stage in the DUN are transferred between stages to boost the performance. Our extensive experiments on three widely considered datasets demonstrate the promising performance of LR-CSNet compared to state-of-the-art methods in natural image CS.
U2 - 10.1109/ICCC56324.2022.10065722
DO - 10.1109/ICCC56324.2022.10065722
M3 - Article in proceedings
SP - 1951
EP - 1957
BT - 2022 IEEE International Conference on Computer and Communications (ICCC), Chengdu, China
PB - IEEE
Y2 - 9 December 2022 through 12 December 2022
ER -
ID: 338603504