Gradient and Multi Scale Feature Inspired Deep Blind Gaussian Denoiser

Abstract

In this paper, a novel deep blind Gaussian denoising network is proposed utilizing the concepts of gradient information, multi-scale feature information and feature denoising for removing additive white Gaussian noise (AWGN) from images. The proposed network consists of two modules where in the first module generates an intermediate image whose gradient information is concatenated with the features of second module to generate the final residual image. Subtracting this residual image with the noisy image gives the desired denoised image. The feature denoising block used in the middle of the first module enhances the feature information of the intermediate image. The usage of gradient information of this intermediate denoised image, together with the multi scale feature information block, in the second module further contributes to the quality of the final denoised image. Experimental results show superior denoising performance of the proposed method in comparison to several state of the art classical and learning based blind denoising methods like EPLL, BM3D, WNNM, DnCNN, MemNet, BUIFD, Self2Self and ComplexNet by a decent margin (an improvement of up to 2.4dB in PSNR, 0.07 in SSIM and 0.03 in FOM index with the second best performing model) when experimented over BSD68, Set5, Set14, SunHays80 and Manga109 image databases.