Real-world Image Denoising Based on U-shaped Multi-scale Attention Method
To address the issue of subpar denoising results in existing algorithms for real-world image denoising,we propose an innovative solution called the U-Shape Pyramid Channel Attention(UPCA).The U-shape structure comprises a fusion of multi-scale feature modules and long-range channel attention modules,forming a pyramid attention module.Through concatenation operations,the U-shape structure allows for the fusion of output feature maps from each layer,minimizing the loss of fine-grained image details during the convolution and downsampling processes.The multi-scale feature pyramid module effectively leverages contextual information to restore clean images,while the long-range channel attention module establishes dependencies on global information,thereby enhancing the denoising performance of the network.Additionally,we introduce a noise term in the loss function to expedite convergence during training and improve denoising efficiency.Experimental comparisons on the SIDD and DND datasets demonstrate the feasibility and su-periority of the UPCA.Compared to RIDNet which also utilizes channel attention,UPCA achieves a remarkable improvement of 0.81 dB/0.044 in terms of PSNR/SSIM metrics.The visually enhanced denoised images produced by UPCA are superior,and it requires less computational power for training with the same set of parameters.
万方数据
维普
