Deep learning image denoising based on multi-stage supervised with Res2-Unet

To restore high quality images from different types of noise images，this study developed a multi-stage supervised deep residual（MSDR）neural network based on Res2-Unet-SE. First，using the neural network，the image denoising task was devised as a multi-stage process. Then，in each processing stage，image blocks with different resolutions were input into a Res2-Unet sub-network to obtain feature information at different scales，and an adaptive learning of the feature fusion information was transferred to the next stage through a channel attention mechanism. Finally，the feature information of different scales was superimposed to achieve high-quality image noise reduction. The BSD400 dataset was selected for training in the experiments，and a Gaussian noise reduction test was performed using the Set12 data set. Real noise reduction test was conducted using the SIDD data set. Compared with the common denoising neural network，the peak signal-to-noise ratios（PSNRs）of the proposed denoising convolutional neural network（DnCNN）improved by 0. 03 dB，0. 05 dB，and 0. 14 dB when Gaussian noises of σ = 15，25 and 50，respectively，were added to the image data set. Compared with the latest dual residual block network（DuRN）algorithm，the PSNR of the image denoised using the proposed algorithm was higher by 0. 06 dB，0. 57 dB，and 0. 39 dB，respectively. For images containing real noise，the PSNR of the image denoised by the proposed algorithm was 0. 6 dB higher than that by the convolutional blind denoising network（CBDNET）algorithm. The results indicate that the proposed algorithm is highly robust in the task of image denoising，and it can effectively remove noise and restore the details of an image，as well as fully maintain the global dependence of the image. © 2023 Chinese Academy of Sciences. All rights reserved.