![]() ![]() He is a "Highly Cited Researcher" selected by Thomson Reuters. on Image Processing, SIAM Journal of Imaging Sciences and Image and Vision Computing, etc. Zhang is an Associate Editor of IEEE Trans. As of 2016, his publications have been cited more than 20,000 times in the literature. Zhang has published more than 200 papers in those areas. His research interests include Computer Vision, Pattern Recognition, Image and Video Processing, and Biometrics, etc. Since July 2015, he has been a Full Professor in the same department. In 2006, he joined the Department of Computing, The Hong Kong Polytechnic University, as an Assistant Professor. From January 2003 to January 2006 he worked as a Postdoctoral Fellow in the Department of Electrical and Computer Engineering, McMaster University, Canada. From 2001 to 2002, he was a research associate in the Department of Computing, The Hong Kong Polytechnic University. and Ph.D degrees in Control Theory and Engineering from Northwestern Polytechnical University, Xi’an, P.R. ![]() degree in 1995 from Shenyang Institute of Aeronautical Engineering, Shenyang, P.R. Lei Zhang (M’04, SM’14) received his B.Sc. "The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops" by Computer Vision Foundation Open Access and Published papers The 19 accepted NTIRE workshop papers were published under the book title Please refer to the example egpaper_for_review.pdf The author kit provides a LaTeX2e template for paper submissions. Submission siteĪccepted and presented papers will be published after the conference in CVPR Workshops proceedings together with the CVPR2017 main conference papers. If a paper is submitted also to CVPR and accepted, the paper cannot be published both at the CVPR and the workshop. Dual submission policyĭual submission is allowed with CVPR2017 main conference only. Reviewers do not know the names of the authors. Authors do not know the names of the chair/reviewers of their papers. The paper format must follow the same guidelines as for all CVPR 2017 submissions. ![]() Instructions and Policies Format and paper lengthĪ paper submission has to be in English, in pdf format, and at most 8 pages (excluding references) in double column. The top ranked participants co-author the challenge paper report. The training data is made available to the registered participants. Track 2: unknown downscaling x4 competition.Track 2: unknown downscaling x3 competition.Track 2: unknown downscaling x2 competition.Track 1: bicubic downscaling x4 competition.Track 1: bicubic downscaling x3 competition.Track 1: bicubic downscaling x2 competition.To learn more about the challenge, to participate in the challenge, and to access the newly collected DIV2K dataset with DIVerse 2K resolution images everybody is invited to register at the following links, accordingly: Track 2: unknown assumes that the explicit forms for the degradation operators are unknown, only the training pairs of low and high images are available.Track 1: bicubic uses the bicubic downscaling (Matlab imresize), one of the most common settings from the recent single-image super-resolution literature.We propose a large DIV2K dataset with DIVerse 2K resolution images. In order to gauge the current state-of-the-art in example-based single-image super-resolution, to compare and to promote different solutions we are organizing an NTIRE challenge in conjunction with the CVPR 2017 conference. NTIRE challenge on example-based single image super-resolution The topics include, but are not limited to: Papers addressing topics related to image restoration and enhancement are invited. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations. This workshop aims to provide an overview of the new trends and advances in those areas. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved.Įach step forward eases the use of images by people or computers for the fulfillment of further tasks, with image restoration or enhancement serving as an important frontend. Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Image restoration and image enhancement are key computer vision tasks, aiming at the restoration of degraded image content or the filling in of missing information.
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