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Multiple degree total variation (MDTV) regularization for image restoration
Conference proceeding

Multiple degree total variation (MDTV) regularization for image restoration

Yue Hu, Xin Lu and Mathews Jacob
2016 IEEE International Conference on Image Processing (ICIP), Vol.2016-, pp.1958-1962
09/2016
DOI: 10.1109/ICIP.2016.7532700
PMID: 33628137
url
https://www.ncbi.nlm.nih.gov/pmc/articles/7899932View
Open Access

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 propose an iteratively reweighted majorize minimize algorithm. We demonstrate the utility of the MDTV regularization in the context of image denoising and compressed sensing image reconstruction. We compare the proposed method with the 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 the MDTV penalty provides improved image recovery performance.
Noise Reduction Optimization compressed sensing majorize minimize Multiple degree total variation (MDTV) Standards Image reconstruction HDTV Signal to noise ratio

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