SCNet: A Dual-Branch Network for Strong Noisy Image Denoising Based on Swin Transformer and ConvNeXt

Abstract

Image denoising plays a vital role in restoring high-quality images from noisy inputs and directly impacts downstream vision tasks. Traditional methods often fail under strong noise, causing detail loss or excessive smoothing. While recent Convolutional Neural Networks-based and Transformer-based models have shown progress, they struggle to jointly capture global structure and preserve local details. To address this, we propose SCNet, a dual-branch fusion network tailored for strong-noise denoising. It combines a Swin Transformer branch for global context modeling and a ConvNeXt branch for fine-grained local feature extraction. Their outputs are adaptively merged via a Feature Fusion Block using joint spatial and channel attention, ensuring semantic consistency and texture fidelity. A multi-scale upsampling module and the Charbonnier loss further improve structural accuracy and visual quality. Extensive experiments on four benchmark datasets show that SCNet outperforms state-of-the-art methods, especially under severe noise, and proves effective in real-world tasks such as mural image restoration.