Abstract: Among the existing image denoising methods based on deep learning,there are problems at the network architecture dimension that single-stage network is hard to represents feature dependency and it is difficult to reconstruct clear images in complex scenarios.The internal features of multi-stage networks are not tightly connected and the original image details are easily lost.At the basic building block dimension,there are problems that the convolutional layer is difficult to handle cross-level features at large noise levels,and the fully connected layer is difficult to capture the spatial details of the image locality.To solve the above problems,this paper proposes solutions from two aspects.On the one hand,a novel cross-stage gating feature fusion is proposed at the architecture dimension,so as to better connect the shallow features of the first-stage network with the deep features of the second-stage network,promote the interaction of information flow and make the internal correlation of the denoising network closer,while avoiding the loss of original spatial details.On the other hand,a dual-axis shifted block combining convolu-tional neural network(CNN) and multi-layer perceptron(MLP) is proposed,which is applied to low-resolution and multi-channel number feature maps to alleviate the problem of insufficient learning ability of CNN on cross-level feature dependencies in complex noise scenarios.And CNN is used to focus on high-resolution feature maps with low channel number to fully extract the spatial local dependencies of noisy images.Many quantitative and qualitative experiments prove that the proposed algorithm achieves the best peak signal-to-noise ratio(PSNR) and structural similarity(SSIM)denoising indicators with a small number of parameters and computational costs in real-world image denoising and Gaussian noise removal tasks.

Key words: Image processing, Image denoising, Deep learning, Convolutional neural network, Multi-layer perceptron, Feature fusion