Image Smoothing via Unsupervised Learning. Image smoothing represents a fundamental component of many disparate computer vision and graphics applications. In this paper, we present a unified unsupervised (labelfree) learning framework that facilitates generating flexible and highquality smoothing effects by directly learning from data using deep convolutional neural networks (CNNs). The heart of the design is the training signal as a novel energy function that includes an edgepreserving regularizer which helps maintain important yet potentially vulnerable image structures, and a spatiallyadaptive Lp flattening criterion which imposes different forms of regularization onto different image regions for better smoothing quality. We implement a diverse set of image smoothing solutions employing the unified framework targeting various applications such as, image abstraction, pencil sketching, detail enhancement, texture removal and contentaware image manipulation, and obtain results comparable with or better than previous methods. Moreover, our method is extremely fast with a modern GPU (e.g, 200 fps for 1280x720 images). Our codes and model are released in this https URL.

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.