Static and Dynamic-domain Prior Enhancement Two-stage Video Compressed Sensing Reconstruction Network

YANG Chunling; LIANG Ziwen

doi:10.11999/JEIT240295

Volume 46 Issue 11

Nov. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(11): 4247-4258

YANG Chunling, LIANG Ziwen. Static and Dynamic-domain Prior Enhancement Two-stage Video Compressed Sensing Reconstruction Network[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4247-4258. doi: 10.11999/JEIT240295

Citation:

YANG Chunling, LIANG Ziwen. Static and Dynamic-domain Prior Enhancement Two-stage Video Compressed Sensing Reconstruction Network[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4247-4258. doi: 10.11999/JEIT240295

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Static and Dynamic-domain Prior Enhancement Two-stage Video Compressed Sensing Reconstruction Network

doi: 10.11999/JEIT240295

YANG Chunling^,,
LIANG Ziwen

School of Electronic and Information, South China University of Technology, Guangzhou 510000, China

Funds: The Natural Science Foundation of Guangdong Province (2019A1515011949)

Received Date: 2024-04-19
Rev Recd Date: 2024-09-19

Available Online: 2024-10-08

Publish Date: 2024-11-10

Abstract

Abstract

Video compressed sensing reconstruction is a highly underdetermined problem, where the low-quality of initial reconstructed and the single-motion estimation approach limit the effective modeling of inter-frames correlations. To improve video reconstruction performance, the Static and Dynamic-domain Prior Enhancement Two-stage reconstruction Network (SDPETs-Net) is proposed. Firstly, a strategy of reconstructing second-order static-domain residuals using reference frame measurements is proposed, and a corresponding Static-domain Prior Enhancement Network (SPE-Net) is designed to provide a reliable basis for dynamic-domain prior modeling. Secondly, the Pyramid Deformable-convolution Combined with Attention-search Network (PDCA-Net) is designed, which combines the advantages of deformable-convolution and attention mechanisms, and a pyramid cascade structure is constructed to effectively model and utilize dynamic-domain prior knowledge. Lastly, the Multi-Feature Fusion Residual Reconstruction Network (MFRR-Net) extracts and fuses key information of each feature from multiple scales to reconstruct residues, alleviating the instability of model training caused by the coupling of the two stages and suppressing feature degradation. Simulation results show that the Peak Signal-to-Noise Ratio (PSNR) is improved by an average of 3.34 dB compared to the representative two-stage network JDR-TAFA-Net under the UCF101 test set, and by an average of 0.79 dB compared to the recent multi-stage network DMIGAN.
- Video Compressed Sensing (VCS),
- Inter-frame correlation modeling,
- Two-stage reconstruction,
- Feature alignment

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References(29)

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