Premium
Scale‐aware Structure‐Preserving Texture Filtering
Author(s) -
Jeon Junho,
Lee Hyunjoon,
Kang Henry,
Lee Seungyong
Publication year - 2016
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13005
Subject(s) - texture filtering , smoothing , computer science , artificial intelligence , computer vision , image (mathematics) , texture (cosmology) , pixel , kernel (algebra) , scale (ratio) , texture compression , pattern recognition (psychology) , perspective (graphical) , image texture , mathematics , image processing , physics , combinatorics , quantum mechanics
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.