测试技术学报2025，Vol.39Issue(5)：548-557,10.DOI:10.62756/csjs.1671-7449.2025063
基于低秩模型和残差模型的图像降噪
Image Denoising Based on Low Rank Model and Residual Model
摘要
Abstract
Most existing group sparse representations based image restoration methods utilize the non-local self-similarity prior property to cluster similar small blocks into groups and apply sparsity to each group of coefficients,which effectively preserves image texture information.However,these methods only apply simple sparsity to each individual block in the group,but ignore other beneficial image attri-butes.Based on this,an image-denoising algorithm based on a low-rank model and a residual model is proposed.It not only utilizes the sparsity and low rank of each group of similar blocks,but also uses residual learning methods to automatically estimate the true sparse representation of image blocks.The experimental results show that the proposed algorithm fully considers the relationship between blocks,com-bines the correlation and specificity of blocks and then effectively performs image denoising to obtain high-quality restored images.The experimental results also show that the PSNR average gain of the proposed algorithm was 0.34 dB higher than BM3D,0.48 dB higher than NCSR,0.2 dB higher than LRJS,0.04 dB higher than LGSR and GSR-SRLR,and the average SSIM value reached the second highest,which is sufficient to prove that it is superior to many popular or state-of-the-art denoising algorithms.关键词
图像去噪/稀疏表示/非局部自相似/交替最小化Key words
image denoising/sparse representation/nonlocal self-similarity/alternating minimization分类
信息技术与安全科学引用本文复制引用
杨雅兰,胡红萍,杨正民..基于低秩模型和残差模型的图像降噪[J].测试技术学报,2025,39(5):548-557,10.基金项目
山西省基础研究计划资助项目(20210302123019,202103021224195,202103021224212,202103021223189) （20210302123019,202103021224195,202103021224212,202103021223189）
山西省回国留学人员科研项目(2021-108) （2021-108）
