Single image denoising with a feature-enhanced network

Recent Transformer-based networks have shown impressive performance on single image denoising tasks. While the Transformer model promotes the interaction of long-range features, it generally involves high computational complexity. In this paper, we propose a feature-enhanced denoising network (FEDNet) by combining CNN architectures with Transformers. Specifically, we propose an effective cross-channel attention to boost the interaction of channel information and enhance channel features. In order to fully exploit image features, we incorporate Transformer blocks into minimum-scale layers of the network, which can not only capture the long-distance dependency of low-resolution features but also reduce the multiplier-accumulator operations (MACs). Meanwhile, a structure-preserving block is designed to enhance the structural feature extraction. Experimental results on both synthetic and real-world datasets demonstrate that our model can achieve the state-of-the-art denoising performance with low computational costs.