Nonlocal Adaptive Direction-Guided Structure Tensor Total Variation For Image Recovery
This is an incremental improvement for image processing, addressing the challenge of capturing directional information in variational methods.
The paper tackled image recovery by enhancing the nonlocal structure tensor total variation (NLSTV) regularization with directional priors, using anisotropic Gaussian kernels to estimate directions from corrupted images, and achieved better results than NLSTV and other models in visual and quantitative evaluations.
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.