Abstract: In order to address the problems of singularization of image denoising feature extraction and low feature utilization,which cannot generate clearer images,a cascaded multi-scale feature fusion residual real image denoising network is proposed.The network's dual-branch adaptive dense residual block uses dual-path asymmetric dilation convolution to expand the image receptive field to selectively extract rich texture features on the horizontal scale.In the multi-scale space U-Net module,the multi-scale space fusion block is used to enhance the network's learning ability of the overall image structure,learn different levels of information,and acquire multi-level features based on image space and context information.Skip connections facilitates parameter sharing among structures,fully integrating features at different scales and ensuring the integrity of information.Finally,dual residual learning is used to generate clear denoised images.Results show that the peak signal-to-noise ratio of the proposed algorithm on real noise datasets(DND and SIDD) is 39.68 dB and 39.50 dB respectively,and the structural similarity is 0.953 and 0.957 respectively,which is better than the mainstream denoising algorithm.The proposed algorithm enhances denoising performance while retaining more detailed information,further improving image quality.

Key words: Image denoising, Real noise, Convolutional neural network, Multi-scale feature fusion, Dense residual