Extending Guided Image Filtering for High-Dimensional Signals

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 693)

Abstract

This paper presents an extended method of guided image filtering (GF) for high-dimensional signals and proposes various applications for it. The important properties of GF include edge-preserving filtering, local linearity in a filtering kernel region, and the ability of constant time filtering in any kernel radius. GF can suffer from noise caused by violations of the local linearity when the kernel radius is large. Moreover, unexpected noise and complex textures can further degrade the local linearity. We propose high-dimensional guided image filtering (HGF) and a novel framework named combining guidance filtering (CGF). Experimental results show that HGF and CGF can work robustly and efficiently for various applications in image processing.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Nagoya UniversityNagoyaJapan
  2. 2.Nagoya Institute of TechnologyNagoyaJapan

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