Nonlocal Adaptive Direction-Guided Structure Tensor Total Variation For Image Recovery
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ID: 283451
2020
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Abstract
A common strategy in variational image recovery is utilizing the nonlocal
self-similarity (NSS) property, when designing energy functionals. One such
contribution is nonlocal structure tensor total variation (NLSTV), which lies
at the core of this study. This paper is concerned with boosting the NLSTV
regularization term through the use of directional priors. More specifically,
NLSTV is leveraged so that, at each image point, it gains more sensitivity in
the direction that is presumed to have the minimum local variation. The actual
difficulty here is capturing this directional information from the corrupted
image. In this regard, we propose a method that employs anisotropic Gaussian
kernels to estimate directional features to be later used by our proposed
model. The experiments validate that our entire two-stage framework achieves
better results than the NLSTV model and two other competing local models, in
terms of visual and quantitative evaluation.
| Reference Key |
kamasak2020nonlocal
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|---|---|
| Authors | Ezgi Demircan-Tureyen; Mustafa E. Kamasak |
| Journal | arXiv |
| Year | 2020 |
| DOI |
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