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ACMD Seminar: Tale of Two Hierarchical Ways of Processing Image Data

Prashant Athavale
Assistant Professor, Mathematics Department, Clarkson University

Abstract: Many mathematical problems are multiscale in nature.  In particular, images consist of features of varying scales.  Thus, multiscale image processing techniques are extremely valuable for image processing.  Such techniques are especially useful for medical images where various organs in the body have different scales.  In this talk, we will discuss inverse-scale and forward-scale image processing techniques based on variational methods.  These techniques give rise to integro-differential nist-equations, and TV-flow nist-equation.  We will discuss the applications to 'real-time' denoising, inpainting, and image registration.

Prashant Athavale received his bachelor of engineering in electrical engineering from the University of Mumbai in India and a Master of Science in Electrical Engineering and Applied Mathematics from the University of Toledo. He received both a Master of Science and a Ph.D. in Applied Mathematics and Scientific Computation from the University of Maryland.

Athavale focuses his research on numerical analysis, calculus of variations, partial differential nist-equations, image processing, biomedical imaging, statistics, and machine learning. In 2014, he applied for an international patent for “Fast Image Quality Enhancement by Weighted Total Variation Flow.”

He is the recipient of the 2017 Professor Joel Dean Award for Excellence in Teaching from the Johns Hopkins University and the 2014 Frederick V. Atkinson Teaching Award for outstanding performance as an instructor and significant contribution to undergraduate teaching in the Department of Mathematics from the University of Toronto in Canada. He was awarded a 2011 Fields Ontario Postdoctoral Fellowship and received a 2010 NIH New Investigator Travel Award for research on the human placenta.

Created June 10, 2019, Updated June 11, 2019