Scale-aware Structure-Preserving Texture Filtering

Abstract

This paper presents a novel method to enhance the performance of structure-preserving image and texture filtering. With conventional edge-aware filters, it is often challenging to handle images of high complexity where features of multiple scales coexist. In particular, it is not always easy to find the right balance between removing unimportant details and protecting important features when they come in multiple sizes, shapes, and contrasts. Unlike previous approaches, we address this issue from the perspective of adaptive kernel scales. Relying on patch-based statistics, our method identifies texture from structure and also finds an optimal per-pixel smoothing scale. We show that the proposed mechanism helps achieve enhanced image/texture filtering performance in terms of protecting the prominent geometric structures in the image, such as edges and corners, and keeping them sharp even after significant smoothing of the original signal.