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IEEE JSTSP SI 2020 : IEEE JSTSP Deep Learning for Image/Video Restoration and Compression (Special Issue on) | |||||||||||||||
Link: https://signalprocessingsociety.org/blog/ieee-jstsp-special-issue-deep-learning-imagevideo-restoration-and-compression | |||||||||||||||
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Call For Papers | |||||||||||||||
IEEE Journal of Selected Topics in Signal Processing Deep Learning(IF: 6.688, 19/265)
Call for Papers: IEEE Journal of Selected Topics in Signal Processing Special Issue on Deep Learning for Image/Video Restoration and Compression The huge success of deep-learning-based approaches in computer vision inspired research in learned solutions to classic image/video processing problems, such as denoising, deblurring, super-resolution, and compression. Hence, learning based methods have emerged as a promising nonlinear signal processing framework for image/video restoration and compression. Recent works have shown that learned models can achieve significant performance gains over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. Yet, compelling research challenges still remain to be addressed. These include: i) learned models contain millions of parameters, which makes real-time inference on common devices a challenge, ii) it is difficult to interpret learned models or to provide performance bounds on results, iii) it is important to provide a loss function, for training, that accurately reflects human perception of quality, and iv) the performance of learned models trained on synthetically generated data drops sharply on real-world images/video, where the quantity and quality of training data is limited. This special issue invites original contributions in innovative architectures and training methods for effective and efficient networks for image/video restoration and compression to address these and other challenges. Topics of interest include (but are not limited to): ● New architectures for image and video restoration, including super-resolution, denoising, deblurring, dehazing, and inpainting. ● Novel learned methods for motion compensation and image/video compression. ● Computationally efficient networks for image/video restoration and compression. ● Explainable deep learning for image/video restoration and compression. ● Training with novel loss functions that accurately reflects human perception of quality. ● Robust methods on real-world image/video, where the training data is noisy and/or available training data is limited. Submission Guidelines: Prospective authors should follow the instructions on the IEEE JSTSP webpage https://signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signal-processing and submit their manuscripts at http://mc.manuscriptcentral.com/jstsp-ieee Important Dates: Manuscript Submission Due: 1 July 2020 (EXTENDED) First Review Completed: 1 September 2020 Revised Manuscript Due: 15 October 2020 Second Review Completed: 1 December 2020 Final Manuscript Due: 1 January 2021 Publication Date: February 2021 Guest Editors: A. Murat Tekalp (Koç University, Istanbul, Turkey, mtekalp@ku.edu.tr), Lead GE Michele Covell (Google Research, USA, covell@google.com) Radu Timofte (ETH Zurich, Switzerland, Radu.Timofte@vision.ee.ethz.ch) Chao Dong (Shenzhen Institute of Advanced Technology, China, chao.dong@siat.ac.cn) Links: https://signalprocessingsociety.org/blog/ieee-jstsp-special-issue-deep-learning-imagevideo-restoration-and-compression https://signalprocessingsociety.org/sites/default/files/uploads/special_issues_deadlines/JSTSP_SI_Deep_Learning_Image.pdf |
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