Efficient minimization method for a generalized total variation functional

Paul Rodríguez, Brendt Wohlberg

Research output: Contribution to journalArticlepeer-review

213 Scopus citations

Abstract

Replacing the ℓ2 data fidelity term of the standard Total Variation (TV) functional with an ℓ1 data fidelity term has been found to offer a number of theoretical and practical benefits. Efficient algorithms for minimizing this ℓ1-TV functional have only recently begun to be developed, the fastest of which exploit graph representations, and are restricted to the denoising problem. We describe an alternative approach that minimizes a generalized TV functional, including both ℓ2-TV and ℓM1 -TV as special cases, and is capable of solving more general inverse problems than denoising (e.g., deconvolution). This algorithm is competitive with the graph-based methods in the denoising case, and is the fastest algorithm of which we are aware for general inverse problems involving a nontrivial forward linear operator.

Original languageEnglish
Pages (from-to)322-332
Number of pages11
JournalIEEE Transactions on Image Processing
Volume18
Issue number2
DOIs
StatePublished - 2009

Keywords

  • Image restoration
  • Inverse problem
  • Regularization
  • Total variation

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