Adaptive image processing: a bilevel structure learning approach for mixed-order total variation regularizers

2019/03/16 by Pan Liu, Liu, Pan
Computer Science · Engineering · #26B30 #47J20 #94A08 #Analysis of PDEs (math.AP) #FOS: Mathematics #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1903.06911

openalex publication_date 2019/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A class of mixed-order PDE-constraint regularizer for image processing problem is proposed, generalizing the standard first order total variation (TV). A semi-supervised (bilevel) training scheme, which provides a simultaneous optimization with respect to parameters and the new class of regularizers, is studied. Also, A finite approximation method, which used to solve the global optimization solutions of such training scheme, is introduced and analyzed.

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