Image Compression Sensing Reconstruction Based on Multi-Scale Feature Fusion
Image compressed sensing(CS)reconstruction method aims to restore the sampled image to a high-quality image.At present,CS reconstruction algorithm based on deep learning has superior performance in reconstruction quality and speed,but it has the problem of poor image reconstruction quality at low sampling rate.Therefore,an image CS reconstruction network based on multi-scale attention fusion is pro-posed.Multiple multi-scale residual blocks are introduced into the network to extract the information of different sizes of images,and the spa-tial attention of each multi-scale residual block and the channel attention of dense residual blocks are fused.The local features and global de-pendencies are adaptively integrated to improve the quality of image reconstruction.Experimental results show that the proposed algorithm is superior to other classical methods in PSNR and SSIM,and has better reconstruction performance.
国家科技期刊平台
NSTL
万方数据
维普
