A new blind image denoising method based on asymmetric generative adversarial network

Abstract

Image denoising is a classical topic in computer vision. In recent years, with the development of deep learning, image denoising methods based on discriminative learning have received more attention. In this paper, a new blind image denoising method based on the asymmetric generative adversarial network (ID-AGAN) is proposed. In the new method, the adversarial learning is used to optimise the high-dimensional image information denoising, so as to balance the noise removal and detail retention. In order to overcome the unstability of the GAN training and improve the discriminative ability of the discriminating model, an image downsampling layer is added between the generating model and the discriminating model. Moreover, a multi-scale feature downsampling layer is utilised to extract the feature of the entire image and reducing the effect of noise on training images. Extensive experiments are conducted to verify the performance of the ID-AGAN algorithm. The results demonstrate that authors' method has high performance and flexibility.