Residual Swin Transformer Channel Attention Network for Image Demosaicing

TL;DR

This paper introduces RSTCANet, a novel Swin Transformer-based network for image demosaicing that outperforms existing methods in accuracy while using fewer parameters.

Contribution

The paper proposes RSTCANet, a new transformer-based architecture with residual channel attention blocks, improving demosaicing performance and efficiency over prior neural network approaches.

Findings

RSTCANet achieves superior demosaicing quality compared to state-of-the-art methods.

The model has fewer parameters, indicating higher efficiency.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

Image demosaicing is problem of interpolating full- resolution color images from raw sensor (color filter array) data. During last decade, deep neural networks have been widely used in image restoration, and in particular, in demosaicing, attaining significant performance improvement. In recent years, vision transformers have been designed and successfully used in various computer vision applications. One of the recent methods of image restoration based on a Swin Transformer (ST), SwinIR, demonstrates state-of-the-art performance with a smaller number of parameters than neural network-based methods. Inspired by the success of SwinIR, we propose in this paper a novel Swin Transformer-based network for image demosaicing, called RSTCANet. To extract image features, RSTCANet stacks several residual Swin Transformer Channel Attention blocks (RSTCAB), introducing the channel attention for each two successive ST blocks. Extensive experiments demonstrate that RSTCANet out- performs state-of-the-art image demosaicing methods, and has a smaller number of parameters.