J
Jian Sun
Researcher at Xi'an Jiaotong University
Publications - 394
Citations - 356427
Jian Sun is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 109, co-authored 360 publications receiving 239387 citations. Previous affiliations of Jian Sun include French Institute for Research in Computer Science and Automation & Tsinghua University.
Papers
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Journal ArticleDOI
Progressive inter-scale and intra-scale non-blind image deconvolution
TL;DR: Zhang et al. as mentioned in this paper presented a progressive inter-scale and intra-scale non-blind image deconvolution approach that significantly reduces ringing. But their approach is built on a novel edge-preserving algorithm called bilateral Richardson-Lucy (BRL) which uses a large spatial support to handle large blur.
Posted Content
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
TL;DR: A deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN) is proposed and the candidate set of motion kernels predicted by the CNN are extended using carefully designed image rotations.
Book ChapterDOI
Background cut
TL;DR: Zhang et al. as mentioned in this paper proposed a real-time foreground layer extraction algorithm based on background contrast attenuation, which adaptively attenuates the contrasts in the background while preserving the contrasts across foreground/background boundaries.
Posted Content
DetNet: A Backbone network for Object Detection.
TL;DR: State-of-the-art results have been obtained for both object detection and instance segmentation on the MSCOCO benchmark based on the DetNet~(4.8G FLOPs) backbone.
Book ChapterDOI
Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing
Dong Yang,Jian Sun +1 more
TL;DR: A novel deep learning approach for single image dehazing by learning dark channel and transmission priors and incorporating haze-related prior learning into deep network is proposed.