软件导刊2024，Vol.23Issue(1)：156-160,5.DOI:10.11907/rjdk.231013
基于多尺度特征融合的图像压缩感知重构
Image Compression Sensing Reconstruction Based on Multi-Scale Feature Fusion
摘要
Abstract
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.关键词
压缩感知/注意力机制/深度学习/多尺度特征提取Key words
compression sensing/attention mechanism/deep learning/multi-scale feature extraction分类
计算机与自动化引用本文复制引用
何卓豪,宋甫元,陆越..基于多尺度特征融合的图像压缩感知重构[J].软件导刊,2024,23(1):156-160,5.基金项目
国家自然科学基金项目(62172232) （62172232）
