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

arXiv 2305.12986 ↗First posted 22 May 2023cs.CV cs.MM eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 22 May 2023.

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22 of 58 references.

References · 22

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,093
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,469
  3. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,876
  4. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,354
  5. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,259
  6. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  7. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 469
  8. 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 408
  9. 2019Attention Augmented Convolutional Networkscited by 363
  10. 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 195
  11. 2018Multi-level Wavelet-CNN for Image Restorationcited by 184
  12. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 120
10 more
  1. 2018Rank Minimization for Snapshot Compressive Imagingcited by 99
  2. 2014From Denoising to Compressed Sensingcited by 68
  3. 2020AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensingcited by 68
  4. 2017Learned D-AMP: Principled Neural Network based Compressive Image Recoverycited by 65
  5. 2017Deep Networks for Compressed Image Sensingcited by 23
  6. 2019Multi-Channel Deep Networks for Block-Based Image Compressive Sensingcited by 22
  7. 2022Content-aware Scalable Deep Compressed Sensingcited by 21
  8. 2017Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Imagescited by 14
  9. 2022Dense residual Transformer for image denoisingcited by 6
  10. 2020D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRIcited by 5

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