基于伪监督注意力短期记忆与多尺度去伪影网络的图像分块压缩感知

基于深度展开网络的分块压缩感知(BCS)方法，在迭代去块伪影时通常会同时去除部分信号和保留部分块伪影，不利于信号恢复。为了改善重建性能，在学习去噪的迭代阈值(LDIT) 算法基础上，该文提出基于伪监督注意力短期记忆与多尺度去伪影网络(MSD-Net)的图像BCS迭代方法(PSASM-MD)。首先，在每步迭代中，利用残差网络并行地对每个图像子块单独去噪后再拼接。然后，对拼接后的图像采用含有伪监督注意力模块(PSAM)的MSD-Net进行特征提取，以更好地去除块伪影以提高重建性能。其中，PSAM被用于从含有块伪影的残差中抽取部分有用信号，并传递到下一步迭代实现短期记忆，以尽量避免去除有用信号。实验结果表明，该文方法相比现有先进的同类BCS方法在主观视觉感知和客观评价指标上均取得了更优的结果。

Pseudo Supervised Attention Short-term Memory and Multi-Scale Deartifacting Network Based on Image Block Compressed Sensing

Deep unfolding network based Block Compressed Sensing (BCS) methods typically remove some signal and retain certain block artifacts simultaneously during iterative deartifacting, which is unfavorable for signal recovery. To enhance reconstruction performance, based on Learned Denoising Iterative Thresholding (LDIT) algorithm. Pseudo Supervised Attention Short-term Memory and Multi-scale Deartifacting (PSASM-MD) based image BCS, is proposed in this paper. Initially, in each iteration, each image block is denoised separately in parallel using residual networks before being concatenated. Subsequently, in conjunction with the Pseudo-Supervised Attention Module (PSAM), Multi-Scale Deartifacting Network (MSD-Net) is used to perform feature extraction on the concatenated images, enabling more efficient removal of block artifacts and improving the reconstruction performance. In this case, PSAM is utilized to extract useful signal components from the residuals containing block artifacts, transfer the short-term memory to the subsequent iteration to minimize the removal of useful signals. Experimental results demonstrate that this approach outperforms existing state-of-the-art BCS methods both in subjective visual perception and objective evaluation metrics.