Speaker: Xiaolin Wu, Department of Electrical and Computer Engineering, McMaster University, Canada
Date: July 26, 2019
Time: 2:00 p.m.
Location: meeting room 335, N5 Building, Qingdao Campus
Sponsor: the School of Information Science and Engineering
A great number of invaluable historical photographs unfortunately only exist in the form of halftone prints in old publications such as newspapers or books. Their original continuous-tone films have long been lost or irreparably damaged. There have been attempts to digitally restore these vintage halftone prints to the original film quality or higher. However, even using powerful deep convolutional neural networks, it is still difficult to obtain satisfactory results. The main challenge is that the degradation process is complex and compounded while little to no real data is available for machine learning. In this research, we develop a novel strategy of two-stage deep learning, in which the restoration task is divided into two stages: the removal of printing artifacts and the inverse of halftoning. The advantage of our technique is that only the simple first stage, which makes the method adapt to real halftone prints, requires unsupervised training, while the more complex second stage of inverse halftoning uses paired synthetic training data entirely. This two-stage training idea is general and can be employed in solving various real image restoration problems. Through extensive experiments we show that, not only does the new technique outperform the existing ones significantly in restoring real halftone prints, but it also works very effectively for real image super-resolution, low light enhancement, etc.
Xiaolin Wu, Ph.D. in computer science, University of Calgary, Canada, 1988. Dr. Wu started his academic career in 1988, and has since been on the faculty of Western University, New York Polytechnic University (NYU Poly), and currently McMaster University. He holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing, computer vision multimedia signal coding and communication, joint source-channel coding, multiple description coding, and network-aware visual communication. He has published over two hundred-sixty research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow, McMaster Distinguished Engineering Professor, an associated editor of IEEE Transactions on Image Processing, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors.
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Edited by Wei Zhen