Adaptive and Dynamic Regularization for Rolling Guidance Image Filtering

Abstract

Separating shapes and textures of digital images at different scales is useful in computer graphics. The Rolling Guidance (RG) filter, which removes structures smaller than a specified scale while preserving salient edges, has attracted considerable attention. Conventional RG-based filters have some drawbacks, including smoothness/sharpness quality dependence on scale and non-uniform convergence. This paper proposes a novel RG-based image filter that has more stable filtering quality at varying scales. Our filtering approach is an adaptive and dynamic regularization for a recursive regression model in the RG framework to produce more edge saliency and appropriate scale convergence. Our numerical experiments demonstrated filtering results with uniform convergence and high accuracy for varying scales.