A fundamental concept in solving inverse problems is the use of regularizers, which yield more physical and less-oscillatory solutions. Total variation (TV) has been widely used as an edge-preserving regularizer. However, objects are often over-regularized by TV, becoming blob-like convex structures of low curvature. This was explained by Andreu et al. by analyzing eigenfunctions of the TV subgradient operator. A compelling approach to better preserve structures is to use anisotropic functionals, which adapt the regularization in an image-driven manner, with strong regularization along edges and low across them. Adaptive anisotropic TV (A^2TV) was successfully used in several studies in the past decade. However, until now there was no theory formulating the type of structures which can be perfectly preserved (eigenfunctions induced by A^2TV). In this study, we address this question.
BIO: Shai Biton received his B.Sc. degree from the Department of Electrical Engineering, summa cum laude, from Ort Braude, Karmiel, in 2011. Currently, he pursues his M.Sc. degree in Electrical Engineering from the Technion – Israel Institute of Technology, Haifa. His current research interests are medical imaging, thermal imaging, image reconstruction, and deep learning.
{*}M.Sc. student under the supervision of Prof. Guy Gilboa.
The lecture will take place on Wednesday, 23/01/2019
at 14:30 in room 1061 of the Andre and Bella Meyer Building.
Department of Electrical Engineering, The Technion, Haifa.
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