Blind denoising using dense hybrid convolutional network

Abstract

The performance of existing deep convolutional networks is limited when encountering images with different noise levels. In this study, a denoising method with state-of-the-art performance that combines a deep convolutional network with the traditional nonlocal mean denoising method is proposed. The noisy image is first denoised using the nonlocal mean method. Then, the denoised image is input into the proposed dense hybrid convolutional network to be trained, producing a clean image with clear details. The dense hybrid convolutional network comprises three parts: a feature-extracting noise-suppressing module that extracts abstract features from denoised images and suppresses the residual noise by interval convolution; a feature-learning module used for training blurred edges and textures; and a magnifying module that uses deconvolution to restore the feature maps to the original size and reduce the noise again. In contrast to existing denoising algorithms, the method has two desirable properties: 1) it can restore edges and textures clearly while removing the noise; 2) it effectively deals with noise of unknown levels (i.e. blind denoising) with a single network model. The conducted experiments show that the proposed method achieves superior performance compared to those of state-of-the-art denoising methods.