Work

Multiple degree total variation (MDTV) regularization for image restoration

Year: 2016

Type: conference-paper

Abstract: We introduce a novel image regularization termed as multiple degree total variation (MDTV). This type of regularization combines the first and second degree directional derivatives, thus providing a good balance between preservation of edges and region smoothness. In order to solve the resulting optimization problem, we proposed a fast majorize minimize algorithm. We demonstrate the utility of the MDTV regularization in the context of image denoising and compressed sensing. We compare the proposed method with standard TV, and the state of the art higher degree methods, including higher degree total variation (HDTV) and total generalized variation (TGV) based schemes. Numerical results indicate that MDTV penalty provides improved image recovery performance.

Source: Proceedings - International Conference on Image Processing

DOI: 10.1109/icip.2016.7532700

Authors: Yue Hu, Xin Lu, Mathews Jacob

Institutions: Harbin Institute of Technology, 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 Institute of Biomedical Imaging and Bioengineering

Award: Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI