集成技术2025，Vol.14Issue(2)：46-57,12.DOI:10.12146/j.issn.2095-3135.20241224001
面向无监督去噪模型的高效采样方法
Efficient Sampling Method for Unsupervised Denoising Model
摘要
Abstract
Image denoising methods based on deep learning have effectively solved the problems of cumbersome parameter tuning and complex noise modeling in traditional denoising methods.However,the model training of supervised learning relies heavily on pairs of clean and noisy images,which limits the wide application of such models.Unsupervised learning denoising models only require single noisy images for training,but the existing unsupervised denoising methods still have the problem that it is difficult to balance network training efficiency and denoising performance.This paper proposes an efficient image denoising method,which improves the efficiency of denoising model training.Specifically,this method proposes a deep neighbor downsampler,which is used to obtain similar image pairs for training the noise model from the same noisy image.The research proposed sampler method not only meets the requirements that the pixels of the image pairs are adjacent and the appearances are similar,but also the deep neighbor downsampling discards some redundant information and avoids heavy dependence on assumptions about the noise distribution.Finally,the research verify the effectiveness of the research method through synthetic experiments with various noise distributions in the standard red green blue space and real image experiments.The experimental results confirm that the sampling strategy the research proposed effectively overcomes the balance problem between training efficiency and denoising performance.关键词
图像去噪/无监督/下采样Key words
image denoising/unsupervised/downsampling分类
计算机与自动化引用本文复制引用
芮浩晖,聂泽东,曾光,秦文健..面向无监督去噪模型的高效采样方法[J].集成技术,2025,14(2):46-57,12.基金项目
国家重点研发计划青年科学家项目(2023YFF0723400) This work is supported by National Key Research and Development Program Young Scientist Project(2023YFF0723400) （2023YFF0723400）
