基于多级残差信息蒸馏的真实图像去噪方法OA北大核心CSTPCD

Deep neural networks have a strong denoising ability for real images and can learn complex nonlinear mapping relationships between noisy and clean images.However,excessive convolution operations result in increased computational costs and occupy a large amount of memory,limiting the application of denoising techniques in low computing power devices.Existing denoising algorithms are prone to damaging detail information,and restoring images may suffer from problems such as excessively smooth edges,missing textures,and residual noise.To address these issues,construct a Multi-level Residual Information Distillation Block(MRIDB)is constructed in this study.By segmenting feature channels,retaining some features for subsequent multi-level fusion,and further extracting refined feature information through depth extraction units.It introduces a Contrast-aware Channel Attention(CCA)mechanism to assign weights to features of different channels.It uses multi-level skip connections to fully integrate contextual information extracted from different stages.It builds a lightweight Multi-level Residual Information Distillation Network(MIRDN)using a low inter-block complexity encoding-decoding structure.The encoding part is a noisy image feature extraction module,and whereas the decoding part is a clean image restoration module.In order to accelerate the training speed,a progressive training method with mixed image sizes is adopted.The experimental results show demonstrate that the Peak Signal-to-Noise Ratios(PSNR)of the proposed method on SSID and DND real image datasets are 39.43 dB and 39.49 dB,respectively.These are improvements in the ranges of 0.17-15.77 dB and 0.02-7.06 dB,respectively,compared with other networks,while and the model parameter count is only 6.92×106.The proposed model has fewer parameters while improving denoising performance.