自适应方向引导的结构张量总变分
Adaptive Direction-Guided Structure Tensor Total Variation
AI总结：
针对DSTV无法处理多方向图像的问题，提出两阶段自适应框架，利用方向描述符引导STV编码一阶信息，并通过高效预处理器估计局部几何，实验证明其在恢复质量和效率上优于现有去噪模型。
摘要：
方向引导的结构张量总变分（DSTV）是最近提出的一种正则化项，旨在提高结构张量总变分（STV）对朝向预定方向变化的敏感性。尽管在单方向图像上获得了看似合理的结果，但DSTV模型不适用于真实世界的多方向图像。在本研究中，我们构建了一个两阶段框架，为DSTV带来自适应性。我们设计了STV的一种替代方案，该方案在空间变化的方向描述符（即方向和各向异性程度）的引导下，编码局部邻域内的一阶信息。为了估计这些描述符，我们提出了一种高效的预处理器，基于结构张量捕获局部几何结构。通过大量实验，我们通过将所提方法与最先进的基于分析的去噪模型在恢复质量和计算效率两方面进行比较，证明了在STV中引入方向信息的有益性。
英文摘要：
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 multi-directional images of real-world. In this study, we build a two-stage framework that brings adaptivity to DSTV. We design an 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 restoration quality and computational efficiency.
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