Journal
SIAM JOURNAL ON IMAGING SCIENCES
Volume 8, Issue 2, Pages 1090-1122Publisher
SIAM PUBLICATIONS
DOI: 10.1137/14098154X
Keywords
structure tensor; patch-based Jacobian; image reconstruction; convex optimization; total variation; inverse problems
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Funding
- Swiss National Science Foundation (SNF) [P300P2_151325]
- European Research Council Starting Grant [204871-HUMANIS]
- COGNIMUSE under the ARISTEIA Action
- European Social Fund
- Greek National Resources
- European Research Council [ERC-2010-AdG 267439-FUN-SP]
- Swiss National Science Foundation (SNF) [P300P2_151325] Funding Source: Swiss National Science Foundation (SNF)
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We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure tensor and is suitable for both grayscale and vector-valued images. It generalizes several existing variational penalties, including the total variation seminorm and vectorial extensions of it. Meanwhile, thanks to the structure tensor's ability to capture first-order information around a local neighborhood, the STV functionals can provide more robust measures of image variation. Further, we prove that the STV regularizers are convex while they also satisfy several invariance properties w.r.t. image transformations. These properties qualify them as ideal candidates for imaging applications. In addition, for the discrete version of the STV functionals we derive an equivalent definition that is based on the patch-based Jacobian operator, a novel linear operator which extends the Jacobian matrix. This alternative definition allow us to derive a dual problem formulation. The duality of the problem paves the way for employing robust tools from convex optimization and enables us to design an efficient and parallelizable optimization algorithm. Finally, we present extensive experiments on various inverse imaging problems, where we compare our regularizers with other competing regularization approaches. Our results are shown to be systematically superior, both quantitatively and visually.
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