Research on Multi-Branch Image Denoising Algorithm

In recent years, deep convolutional neural network（CNN）has caused a great sensation in the field of image denoising. However, for the Gaussian image denoising task, it has some problems：（1）most of the single-branch models can not make full use of image features and are often affected by information loss.（2）Most deep CNNs have the problem of insufficient edge feature extraction and performance saturation. In order to solve these two problems, a multibranch network model based on deep learning（MBNet）is proposed. Firstly, in order to solve the problem of insufficient feature extraction of single-branch network model, MBNet introduces multiple different and complementary networks to combine and then perform feature fusion to enhance the denoising effect and generalization ability. Secondly, in order to solve the problem of inadequate edge feature extraction, MBNet introduces multiple cavity convolution with different expansion rates to increase the receptive field and extract more context information. Finally, in order to solve the performance saturation problem of deep CNN, MBNet also adopts multi-local residual learning and global residual learning. A large number of experimental results show that when σ = 15, the average PSNR values of MBNet on Set12, BSD68, CBSD68, Kodak24 and McMaster datasets are 32.981 dB, 31.750 dB, 34.001 dB, 34.709 dB and 34.394 dB, respectively. MBNet has better performance than the current advanced image denoising methods, and can obtain clearer image and edge texture features in subjective visual effects. © 2018 Editorial Office Of Water Saving Irrigation. All rights reserved.