级联离散小波多频带分解注意力图像去噪方法

针对图像去噪网络中下采样导致高频信息损失和细节保留能力差的问题，设计了一种级联离散小波多频带分解注意力图像去噪网络。其中多尺度级联离散小波变换结构将原始图像分解为多个尺度下的高低频子带来代替传统下采样，能减少高频信息损失。多频带特征增强模块使用不同尺度的卷积核并行处理高低频特征，在子网络每一级下重复使用两次，可增强全局和局部的关键特征信息。多频带分解注意力模块通过注意力评估纹理细节成分的重要性并加权不同频带的细节特征，有助于多频带特征增强模块更好地区分噪声和边缘细节。多频带选择特征融合模块融合多尺度多频带特征增强选择性特征，提高模型对于不同尺度噪声的去除能力。在SIDD和DND数据集上，所提方法的PSNR/SSIM指标分别达到了39.35 dB/0.918、39.72 dB/0.955。实验结果表明，所提方法的性能优于主流去噪方法，同时具有更清晰的纹理细节和边缘等视觉效果。

Cascade discrete wavelet multi-band decomposition attention image denoising method

To address the issue of high-frequency information loss and poor detail preservation ability in image denoising networks caused by downsampling, this paper proposed a cascade discrete wavelet multi-band decomposition attention image denoising 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 local key feature information. The multi-band decomposition attention module evaluated the importance of texture detail components 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 proposed method achieves PSNR/SSIM values of 39.35 dB/0.918 and 39.72 dB/0.955 on the SIDD and DND datasets, respectively. The experimental results demonstrate that the proposed method outperforms mainstream denoising methods and produces clearer visual effects, such as texture details and edges.