SCNet: A Dual‐Branch Network for Strong Noisy Image Denoising Based on Swin Transformer and ConvNeXt - Lin, Chuchao; Zou, Changjun; Xu, Hangbin (Computer animation and virtual worlds, 2025)
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. SCNet is a dual‐branch denoising framework that integrates Swin Transformer for global context modeling and ConvNeXt for local detail preservation. Through attention‐based feature fusion and multi‐scale reconstruction, SCNet demonstrates strong robustness under severe noise and exhibits promising performance in real‐world mural restoration.