Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.

方向引导结构张量总变差（DSTV）是最近提出的正则化术语，旨在提高结构张量总变差（STV）对预定方向变化的敏感性。尽管在单向图像上获得了合理的结果，但 DSTV 模型不适用于任意（多向和/或部分非定向）图像。在这项研究中，我们构建了一个两阶段去噪框架，为基于 DSTV 的去噪带来了适应性。我们设计了一个类似于 DSTV 的 STV 替代方案，它在空间变化的方向描述符（即方向和各向异性剂量）的指导下对局部邻域内的一阶信息进行编码。为了估计这些描述符，我们提出了一种高效的预处理器，可以根据结构张量捕获局部几何。通过广泛的实验，我们通过将所提出的方法与最先进的基于分析的去噪模型在质量和计算效率方面进行比较，证明了 STV 中方向信息的参与是多么有益。