| Abstract: | Development of super-resolution methods is dated back to early 1980s, and the effort intensifies greatly in the past two decades. The essential objective of super-resolution is to enhance the quality of a low-resolution image, or low-resolution images, to match that of a high-resolution counterpart. The super-resolution research started with combining information content from multiple low-resolution images and fusing them into an output image that can be clearer and show finer details unavailable in any of the original low-resolution images; this branch of super-resolution research is referred to as multi-frame super-resolution. Then the focus was shifted to the example-based or learning-based approach, in which external high-resolution and low-resolution patch pairs are created and a relationship between the high-resolution and low-resolution patch pairs are learned through a training dataset; this next phrase of development is known as single-image super-resolution. In this talk, the speaker will present a review of the historical development of super-resolution research and discuss its relevance to and some new problems encountered in material research.
报告人简介: Dr. Yu Ding is the Mike and Sugar Barnes Professor of Industrial & Systems Engineering, Professor of Electrical & Computer Engineering, and a member of Texas A&M Institute of Data Science, Texas A&M Energy Institute, and TEES Institute of Manufacturing Systems. Dr. Ding received his Ph.D. degree from the University of Michigan in 2001. Dr. Ding’s research interest is in the area of data and quality science. Dr. Ding is a recipient of the 2018 Texas A&M Engineering Research Impact Award, the recipient of the 2019 IISE Technical Innovation Award, and a Fellow of IISE and ASME.
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