A Hybrid CNN for Image Denoising

Abstract

Deep convolutional neural networks (CNNs) with strong learning abilities have been used in the field of image denoising. However, some CNNs depend on a single deep network to train an image denoising model, which will have poor performance in complex screens. To address this problem, we propose a hybrid denoising CNN (HDCNN). HDCNN is composed of a dilated block (DB), RepVGG block (RVB), feature refinement block (FB), and a single convolution. DB combines a dilated convolution, batch normalization (BN), common convolutions, and activation function of ReLU to obtain more context information. RVB uses parallel combination of convolution, BN, and ReLU to extract complementary width features. FB is used to obtain more accurate information via refining obtained feature from the RVB. A single convolution collaborates a residual learning operation to construct a clean image. These key components make the HDCNN have good performance in image denoising. Experiment shows that the proposed HDCNN enjoys good denoising effect in public data sets.