Dual Decoder Image Denoising Method Based on Attention Mechanism
Most current image denoising algorithms usually lose the details of an image while removing the image noise,especially when the noise intensity is large,and in some cases,distortion appears.Because the current neural network structure generally tends to have a deep-level design,fusing the shallow features of an image with deep features is difficult.To address these issues,an attention-based two-pass decoder approach for image denoising is proposed.First,a Residual Dense Block(RDB)is designed to increase the depth of the network by improving the U-Net network.The RDB increases the depth of the network,effectively improves the stability of the model,and alleviates the problem of gradient disappearance.Second,a dual decoder structure is designed through multi-scale feature extraction performed by decoders of different scales to strengthen the fusion of shallow and deep features.Third,by introducing an attention mechanism in the decoder,the edge information of the image is captured in a targeted manner to enhance the denoising performance of the model.Experiments prove that,compared with existing common image denoising methods,the proposed method not only effectively removes image noise but also better restores image texture details and has a faster denoising rate.Hence,the proposed method obtains better results in both subjective and objective evaluations.
deep learningConvolutional Neural Network(CNN)image denoisingdual decodermulti-scale
万方数据
维普
