  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.
