Mixed Noise Removal by Residual Learning of Deep CNN

Abstract

: Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose a method based on residual learning for the removal of AWGN-IN noise in this paper. By training, our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images. After a series of experiments under different noise settings, the results show that our method is obviously better than the traditional sparse representation and patch based method. Meanwhile, the time of model training and image denoising is greatly reduced.