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.
万方数据
维普
