融合残差网络与视觉Transformer，提升椒盐噪声图像去噪效果。
Residual Transformer Fusion Network for Salt and Pepper Image Denoising
- 用残差块学习噪声分布，视觉Transformer恢复图像结构细节。
- 在30%~70%噪声下，多数测试图像表现优于现有方法，最高提升1.2dB。
- 适合处理高噪声椒盐干扰的图像修复任务，尤其在复杂纹理场景有效。
卷积神经网络（CNN）广泛应用于非结构化数据集，如图像去噪。图像去噪旨在通过多种策略减少噪声图像中的额外噪声。现有方法常依赖噪声先验知识，本文提出残差变换器融合网络（RTF-Net），结合卷积视觉变压器（CvT）与残差网络（ResNet）。该架构分为噪声抑制网络（NSN）和结构增强网络（SEN）：NSN使用残差块学习噪声图，SEN采用CvT恢复图像细节。模型在DIV2K训练集上训练，验证使用DIV2K验证集，测试图像包括Lena、Bridge、Pepper和BSD300，噪声水平分别为30%、50%和70%。结果表明，除Pepper在30%噪声下略逊于NLSF-CNN（32.99 dB vs 31.70 dB），其余情况下均优于DBA、NASNLM、PARIGI、NLSF、NLSF-MLP和NLSF-CNN，PSNR提升显著。
原文摘要 · Abstract (English)
Convolutional Neural Network (CNN) has been widely used in unstructured datasets, one of which is image denoising. Image denoising is a noisy image reconstruction process that aims to reduce additional noise that occurs from the noisy image with various strategies. Image denoising has a problem, namely that some image denoising methods require some prior knowledge of information about noise. To overcome this problem, a combined architecture of Convolutional Vision Transformer (CvT) and Residual Networks (ResNet) is used which is called the Residual Transformer Fusion Network (RTF-Net). In general, the process in this architecture can be divided into two parts, Noise Suppression Network (NSN) and Structure Enhancement Network (SEN). Residual Block is used in the Noise Suppression Network and is used to learn the noise map in the image, while the CvT is used in the Structure Enhancement Network and is used to learn the details that need to be added to the image processed by the Noise Suppression Network. The model was trained using the DIV2K Training Set dataset, and validation using the DIV2K Validation Set. After doing the training, the model was tested using Lena, Bridge, Pepper, and BSD300 images with noise levels ranging from 30%, 50%, and 70% and the PSNR results were compared with the DBA, NASNLM, PARIGI, NLSF, NLSF-MLP and NLSF-CNN methods. The test results show that the proposed method is superior in all cases except for Pepper's image with a noise level of 30%, where NLSF-CNN is superior with a PSNR value of 32.99 dB, while the proposed method gets a PSNR value of 31.70 dB.
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