CCNet: A Cross-Channel Enhanced CNN for Blind Image Denoising

Nowadays, blind image denoising with deep convolutional neural network (CNN) is one of the research hotspots in the field of image denoising. Relying on the convolutional operation and respective field, CNN is excellent in processing local information. However, this also brings the problems of lack of cross-domain interaction and process of global feature information. We incorporate it with transformer's self-attention mechanism and propose a cross-channel enhanced CNN, namely CCNet, for image denoising. CCNet consists of three parts: the backbone encoder (BE), the cross-channel enhancer (CCE), and the backbone decoder (BD). The BE and BD are constructed using the multiscale symmetric network U-Net and use the residual connection block (RCB) as the basic block for image feature extraction and reconstruction. CCE introduces transposed attention, serving as a complement of BE for cross-channel modeling. Meanwhile, we propose a unique gated fusion block (GFB) to fuse the information of these two modules and further feature learning. To improve training, we use random cropping, shuffling, and mixed noise strategies to expand the noise distribution learned by the model, increasing its noise adaptability. Extensive experiments on grayscale images, color images, and real noisy images demonstrate CCNet's strong performance in these tasks.