Residual Transformer Fusion Network for Salt and Pepper Image Denoising

TL;DR

This paper introduces RTF-Net, a novel architecture combining CNN and Transformer components for effective salt-and-pepper image denoising without prior noise knowledge, demonstrating superior performance across various noise levels.

Contribution

The paper proposes a Residual Transformer Fusion Network (RTF-Net) that integrates ResNet and CvT for improved salt-and-pepper image denoising, addressing limitations of prior methods requiring noise prior knowledge.

Findings

RTF-Net outperforms existing methods in PSNR across multiple test images.

The model effectively learns noise maps and image details without prior noise information.

Superior denoising performance at various noise levels, except in specific cases.

Abstract

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.