Guided Adaptive Image Smoothing via Directional Anisotropic Structure Measurement

Abstract

Image smoothing prefers a good metric to identify dominant structures from textures adaptive of intensity contrast. In this paper, we drop on a novel directional anisotropic structure measurement (DASM) toward adaptive image smoothing. With observations on psychological perception regarding anisotropy, non-periodicity and local directionality, DASM can well characterize structures and textures independent on their contrast scales. By using such measurement as constraint, we design a guided adaptive image smoothing scheme by improving extrema localization and envelopes construction in a structure-aware manner. Our approach can well suppresses the staircase-like artifacts and blur of structures that appear in previous methods, which better suits structure-preserving image smoothing task. The algorithm is performed on a space-filling curve as the reduced domain, so it is very fast and much easy to implement in practice. We make comprehensive comparisons with previous state-of-the-art methods for a variety of applications. Experimental results demonstrate the merit using our DASM as metric to identify structures, and the effectiveness and efficiency of our adaptive image smoothing approach to produce commendable results.