Pyramid Real Image Denoising Network While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on realworld noisy images. The main reason is that the realworld noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multiscale denoising stage, pyramid pooling is utilized to extract multiscale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multiscale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with stateoftheart denoisers in terms of both quantitative measure and visual perception quality.

SRGNet: A GRU Based Feature Fusion Network for Image Denoising Image denoising is an essential pretreatment for most of image processing pipelines, which has been extensively studied for several decades. Recently, convolutional neural networks with skip connections show promising performances on image denoising due to their discriminative feature modeling and utilizing of features from former layers. However, they only apply coarse feature fusion strategies like pixelwise addition or concatenation which are insufficient in image denoising. In this work, we propose a novel network architecture to exploit finer feature fusion. Specifically, a module based on gate recurrent unit is introduced into the architecture, fusing features from different layers and adopting finer feature selection at the same time. Experiments on multiple challenging datasets show the effectiveness of the proposed network.