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.

Low Dose CT Image Denoising Using Multilevel Feature Fusion Network and Edge Constraints Lowdose computed tomography image denoising is a challenging task that has been studied by many researchers. Current denoising methods based on deep learning tend to produce a blur effect on the final results, especially at high noise levels, which are prone to oversmoothed edges and loss of details. In this paper, we propose a deep learning approach based on deep convolutional and edge constraints to mitigate these problems. Firstly, to avoid the loss of shallow layers details while obtaining semanticallyricher features information, we use dilated convolution instead of standard convolution, and fusion feature maps of different levels to aggregate information from different receptive field. Secondly, in order to improve the networku0027s ability to distinguish between noise and image content, we have designed an attention block to adaptively recalibrate the information relationship of the fusion feature maps. Finally, we incorporate edge prior knowledge into LDCT image denoising task, guiding the network to pay more attention on texture and structure information by edge constraints loss. Extensive experiments demonstrate that the proposed method achieves significant improvements over the stateoftheart methods.