total variation regularization of matrix-valued images

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ID: 246545
2007
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Abstract
We generalize the total variation restoration model, introduced by Rudin, Osher, and Fatemi in 1992, to matrix-valued data, in particular, to diffusion tensor images (DTIs). Our model is a natural extension of the color total variation model proposed by Blomgren and Chan in 1998. We treat the diffusion matrix D implicitly as the product D=LLT, and work with the elements of L as variables, instead of working directly on the elements of D. This ensures positive definiteness of the tensor during the regularization flow, which is essential when regularizing DTI. We perform numerical experiments on both synthetical data and 3D human brain DTI, and measure the quantitative behavior of the proposed model.
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christiansen2007internationaltotal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Oddvar Christiansen;Tin-Man Lee;Johan Lie;Usha Sinha;Tony F. Chan
Journal Chemistry, an Asian journal
Year 2007
DOI
10.1155/2007/27432
URL
http://dx.doi.org/10.1155/2007/27432
Keywords
medical physics nuclear medicinemedical technology medical radiology

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