Work

A fast majorize minimize algorithm for higher degree total variation regularization

Year: 2013

Type: conference-paper

Abstract: The main focus of this paper is to introduce a computationally efficient algorithm for solving image recovery problems, regularized by the recently introduced higher degree total variation (HDTV) penalties. The anisotropic HDTV penalty is the fully separable L1semi-norm of the directional image derivatives; the use of this penalty is seen to considerably improve image quality in biomedical inverse problems. We introduce a novel majorize minimize algorithm to solve the HDTV optimization problem, thus considerably speeding it over the previous implementation. Specifically, comparisons with previous iterative reweighted algorithm show an approximate ten fold speedup. The new algorithm enables us to obtain reconstructions that are free of patchy artifacts exhibited by classical TV schemes, while being comparable to state of the art total variation regularization schemes in run time.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi.2013.6556478

Authors: Yue Hu, Sathish Ramani, Mathews Jacob

Institutions: University of Rochester, University of Michigan, University of Iowa

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Heart, Lung, and Blood Institute

Award: Novel algorithm for improved contrast enhanced cardiac MRI