Open AccessAdaptive colour‐guided non‐local means algorithm for compound noise reduction of depth maps
Author(s) -
Ibrahim Mostafa M.,
Liu Qiong,
Yang You
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0074
Subject(s) - computer science , computer vision , noise (video) , artificial intelligence , filter (signal processing) , noise reduction , rgb color model , artifact (error) , adaptive filter , depth map , median filter , pixel , pattern recognition (psychology) , algorithm , image (mathematics) , image processing
Depth maps are used to describe object positioning information in three‐dimensional (3D) space, and they are crucial for RGB‐D data representation, which is useful for numerous interactive visual applications. In practice, depth maps are often contaminated by compound noise, including intrinsic noise and missing regions owing to active illumination shadows. As existing noise models cannot describe the above‐mentioned compound noise effectively, the subsequent filter design is a challenging task. In this study, an adaptive colour‐guided non‐local mean (NLM) filter is proposed to address such compound noise. First, the authors classify the depth map into hole and non‐hole pixels. Then, the proposed filter is designed on the basis of the NLM framework, where the colour image is used as a guide prior for hole‐artifact removal. Finally, the authors use a shock filter to effectively address the non‐regularisation of the restored depth map edges and remove the remaining noise. Experiments show that the proposed filter qualitatively and quantitatively outperforms existing colour‐guided and unguided filters. Moreover, the authors verify the superiority of the proposed filter through virtual view synthesis and 3D scene reconstruction applications.