论文标题
非局部自适应方向引导结构张量张量图像恢复的总变化
Nonlocal Adaptive Direction-Guided Structure Tensor Total Variation For Image Recovery
论文作者
论文摘要
在设计能量功能时，变异图像恢复中的一种共同策略是利用非本地自相似性（NSS）属性。这样的贡献之一是非局部结构张量总变异（NLSTV），它是本研究的核心。本文涉及通过使用方向先验来增强NLSTV正则化项。更具体地说，NLSTV具有利用，因此，在每个图像点，它在假定具有最小局部变化的方向上获得了更高的灵敏度。这里的实际困难是从损坏的图像中捕获这些定向信息。在这方面，我们提出了一种采用各向异性高斯内核来估计我们提出的模型后来使用的定向特征的方法。该实验验证了我们的整个两阶段框架在视觉和定量评估方面比NLSTV模型和其他两个相互竞争的本地模型取得更好的结果。
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.
