计算机技术与发展2025，Vol.35Issue(5)：9-15,7.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0063
基于多支路混合掩码的自监督图像去噪算法
Self-supervised Image Denoising with Multi-branch Hybrid Masks
摘要
Abstract
In recent years,deep learning-based image denoising techniques have made significant progress.However,their advantages are difficult to fully exploit when image pairs are hard to obtain.Self-supervised image denoising algorithms,which only require learning from noisy images themselves to achieve noise removal,have become a popular research topic.Given the spatial correlation of pixel signals and the spatial independence of noise signals in images,denoising methods based on blind-spot networks have demonstrated ex-ceptional performance.However,current blind-spot network methods struggle to effectively break the spatial characteristics of noise when dealing with highly correlated noisy regions.To address this challenge,we propose an innovative multi-branch hybrid mask self-supervised image denoising method.By cleverly combining different types of masks,this approach effectively breaks the spatial correlation of noise,and through a spatial self-similarity attention mechanism,deeply mines the missing detailed information due to mask occlusion.Experimental results show that the proposed method exhibits excellent recovery performance when handling highly correlated noise.Moreover,thanks to the use of multi-branch fusion and spatial self-similarity attention,the proposed method outperforms the current state-of-the-art techniques in terms of detail restoration.关键词
图像去噪/自监督学习/盲点网络/多支路混合掩码/空间自相似注意力Key words
image denoising/self-supervised learning/blind-spot network/multi-branch hybrid masks/spatial self-similarity attention分类
计算机与自动化引用本文复制引用
王玥乔,李鑫远..基于多支路混合掩码的自监督图像去噪算法[J].计算机技术与发展,2025,35(5):9-15,7.基金项目
国家自然科学基金(62201458) （62201458）
陕西省秦创原"科学家+工程师"(2023KXJ-241) （2023KXJ-241）
