计算机应用研究2024，Vol.41Issue(1)：288-295,8.DOI:10.19734/j.issn.1001-3695.2023.06.0245
级联离散小波多频带分解注意力图像去噪方法
Cascade discrete wavelet multi-band decomposition attention image denoising method
摘要
Abstract
To address the issue of high-frequency information loss and poor detail preservation ability in image denoising net-works caused by downsampling,this paper proposed a cascade discrete wavelet multi-band decomposition attention image de-noising network.The multi-scale cascade discrete wavelet transform structure decomposed the original image into high and low-frequency sub-bands at multiple scales,replacing traditional downsampling and reducing high-frequency information loss.The multi-band feature enhancement module employed convolutional kernels of different scales to process high and low-frequency features in parallel.By repeating this process twice at each level of the subnetwork,it effectively enhanced both global and lo-cal key feature information.The multi-band decomposition attention module evaluated the importance of texture detail compo-nents through attention and weighted the detail features of different bands,which helped the multi-band feature enhancement module better distinguish between noise and edge details.The multi-band selective feature fusion module fused multi-scale multi-band features to enhance selective features,improving the model's ability to remove noise at different scales.The pro-posed method achieves PSNR/SSIM values of 39.35 dB/0.918 and 39.72 dB/0.955 on the SIDD and DND datasets,respec-tively.The experimental results demonstrate that the proposed method outperforms mainstream denoising methods and produces clearer visual effects,such as texture details and edges.关键词
图像去噪/高频信息/级联离散小波变换/多频带特征增强/多频带分解注意力Key words
image denoising/high-frequency information/cascade discrete wavelet transform/multi-band feature enhance-ment/multi-band decomposition attention分类
信息技术与安全科学引用本文复制引用
王力,李小霞,秦佳敏,朱贺,周颖玥..级联离散小波多频带分解注意力图像去噪方法[J].计算机应用研究,2024,41(1):288-295,8.基金项目
国家自然科学基金资助项目(62071399) （62071399）
四川省科技计划资助项目(2023YFG0262,2021YFG0383) （2023YFG0262,2021YFG0383）
