報告題目：An Image inpainting via a constrained smoothing and dynamic mode decomposition (II)
報 告 人：Young Ju Lee 教授
所在單位：Texas State University
報告時間：2022年08月01日 星期一 上午09:00
報告地點：#騰訊會議：870-235-164
校内聯系人：賈繼偉 jiajiwei@jlu.edu.cn
報告摘要：In this talk, we present an algebraic and graph theoretic (data-based) image inpainting algorithm. The algorithm is designed to reconstruct area or volume data from one- and two- dimensional slice data. More precisely, given one- or two- dimensional slice data, our algorithm begins with a simple algebraic pre-smoothing of the data, constructs low dimensional representation of pre-smoothed data via Dynamic Mode Decomposition, performs initial area or volume reconstruction via interpolation, and finishes with smoothing the outcome using a constraint bilateral smoothing. Numerical experiments including MRI of a three-year-old and a CT scan of a Covid-19 patient, are presented to demonstrate the superiority of the proposed techniques in comparisons with other commercial and published methods. Some further applications we are currently doing will also be presented.
This work is jointly done with Gwanghyun Jo and Ivan Ojeda-Ruiz.
報告人簡介：Young Ju Lee is a Professor at Texas State University, Mathematics Department. He obtained his Ph.D degree at Penn State and had a prior faculty position at UCLA and Rutgers, The State University of New Jersey. His expertise is at the development of fast solver for partial differential equations. His current research focuses on development of structure preserving finite element discretization for PDE systems. His research has been funded by National Science Foundation and American Chemical Society. The current research is being funded by Korea Brain Pool program by National Research Foundation of Korea.
