Adaptive Integration Skip Compensation Neural Networks for Removing Mixed Noise in Image

Abstract

During the process of acquisition and transmission, images are often likely to be corrupted by mixed Gaussian-impulse noise. Among various image denoising methods, most traditional methods can only deal with a single type of noise due to the difficulty of modeling the distribution of the mixed noise. In this paper, we propose a novel mixed Gaussian-impulse noise removal method based on adaptive integration skip compensation Network (Ai-Sc-Net). More concretely, a couple of skip compensation networks (Sc-Net) Sc-Net-AWGN and Sc-Net-IN are trained on Gaussian and Impulse noise datasets separately to deal with the corresponding single type noise. Further, an adaptive integration network (Ai-Net) is used to integrate the two outputs of Sc-Net-AWGN and Sc-Net-IN. The Ai-Sc-Net is then be constructed based on Sc-Net and Ai-Net, which can handle mixed noise. Experimental results in synthetic noise images have shown great improvements over several state-of-the-art mixed noise removal methods.