Open AccessHigh‐quality depth up‐sampling based on multi‐scale SLIC
Author(s) -
Qiao Yiguo,
Jiao Licheng,
Hou Biao
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.4393
Subject(s) - cluster analysis , sampling (signal processing) , computer science , scale (ratio) , artificial intelligence , pattern recognition (psychology) , function (biology) , residual , confusion , algorithm , computer vision , physics , quantum mechanics , psychology , filter (signal processing) , evolutionary biology , psychoanalysis , biology
A multi‐scale simple linear iterative clustering (SLIC)‐based depth up‐sampling method is proposed in order to obtain high‐quality depth maps, especially in the case of high up‐sampling rate. The proposed method is implemented hierarchically, where the high‐resolution image is segmented from coarse to fine by using multi‐scale SLIC superpixels. A depth guided discriminant function is defined to distinguish the validity of the segmented superpixels, and only the valid ones will be interpolated each layer. The experimental results show that the proposed method solves the depth missing and the depth confusion problems largely.