An Image Denoising Method Using Deep Asymmetrical Skip Connection

Image denoising can effectively improve image quality and sensory effect,and is also the premise of image feature extraction and understanding.For the current popular convolution neural network denoising methods,sequentially connected convolution-deconvolution layer will gradually weaken the image noise in the gradient back propagation process,a method of deep asymmetrical skip connection is proposed for image denoising.In this method,several asymmetrical skip convolution-deconvolution operators are designed to effectively learn image details and noise information,the weights of convolution operations with different depths are quantized to enhance image denoising and restoration.The asymmetrical skip connection can make the image noise information be transmitted directly back to the corresponding convolution layers,which has a good inhibition on gradient diffusion.Experiments on a Berkeley Segmentation Dataset BSD300 show that the proposed algorithm can improve both structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)compared with the benchmark method.