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.

翻译：基于学习去噪的近似消息传递（LDAMP）算法在图像压缩感知（CS）任务中引起了广泛关注。然而，该方法存在两个问题：首先，其全局测量模型严重限制了在高维图像上的适用性，而基于块的测量方法则表现出明显的块效应；其次，LDAMP中的去噪器过于简单，现有去噪器在细节恢复能力上存在局限。为解决上述问题并开发一种适用于图像块压缩感知（BCS）的高性能LDAMP方法，本文提出一种新颖的基于稀疏性和系数置换的AMP方法（SCP-AMP），该方法由基于块的采样模块和双域重建模块组成。在采样模块中，SCP-AMP采用基于离散余弦变换（DCT）的稀疏化策略以降低高频系数对重建的影响，随后通过系数置换策略避免块效应。在重建模块中，提出一种结合DCT域噪声校正与像素域去噪的双域AMP方法进行迭代重建。针对去噪器，我们设计了一种多级深度注意力网络（MDANet），通过引入多级特征和多种注意力机制增强纹理细节。大量实验表明，所提出的SCP-AMP方法在视觉感知和客观指标上均优于其他最先进的BCS算法，取得了更高的重建精度。