Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing

• URL: http://arxiv.org/abs/2305.12986v2
• Date: Thu, 17 Aug 2023 11:43:00 GMT
• Title: Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing
• Authors: Junhui Li, Xingsong Hou, Huake Wang, Shuhao Bi
• Abstract summary: We propose a novel sparsity and coefficient permutation-based AMP ( SCP-AMP) method for image block compressed sensing. SCP-AMP achieves better reconstruction accuracy than other state-of-the-art BCS algorithms in terms of both visual perception and objective metrics.
• Score: 10.643269981555859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The learned denoising-based approximate message passing (LDAMP) algorithm has attracted great attention for image compressed sensing (CS) tasks. However, it has two issues: first, its global measurement model severely restricts its applicability to high-dimensional images, and its block-based measurement method exhibits obvious block artifacts; second, the denoiser in the LDAMP is too simple, and existing denoisers have limited ability in detail recovery. In this paper, to overcome the issues and develop a high-performance LDAMP method for image block compressed sensing (BCS), we propose a novel sparsity and coefficient permutation-based AMP (SCP-AMP) method consisting of the block-based sampling and the two-domain reconstruction modules. In the sampling module, SCP-AMP adopts a discrete cosine transform (DCT) based sparsity strategy to reduce the impact of the high-frequency coefficient on the reconstruction, followed by a coefficient permutation strategy to avoid block artifacts. In the reconstruction module, a two-domain AMP method with DCT domain noise correction and pixel domain denoising is proposed for iterative reconstruction. Regarding the denoiser, we proposed a multi-level deep attention network (MDANet) to enhance the texture details by employing multi-level features and multiple attention mechanisms. Extensive experiments demonstrated that the proposed SCP-AMP method achieved better reconstruction accuracy than other state-of-the-art BCS algorithms in terms of both visual perception and objective metrics.

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