Residual learning of deep convolutional neural networks for image denoising

Abstract

Image denoising is a hot topic in many research fields, such as image processing and computer vision. With the development of deep learning, deep neural networks are widely used for image denoising and have achieved good effectiveness. Inspired by the characteristics of feed-forward denoising convolutional neural network (DnCNN) and biological neuron response, we propose a Symmetry-Rectifier Linear Unit (SyReLU) and further offer a corresponding SyReLU activation function, which has a better consistency with biological neuron characteristics in comparison with other activation functions, e.g. Rectifier Linear Unit (ReLU) and Leaky Rectifier Linear Unit(LReLU). Also, in order to denoise image, we use SyReLU activation function for residual learning of CNN (e.g. DnCNN). Specially, the experimental results indicate DnCNN with SyReLU can achieve better effectiveness than DnCNN with other activation functions (e.g.ReLU and LReLU) for image denosing on Set12 and BSD68 datasets. Briefly, the proposed method plays an important role in the development of activation function and is very useful in deep neural networks for image denosing.