Depth Estimation for Lytro Images by Adaptive Window Matching on EPI

A depth estimation algorithm from plenoptic images is presented. There are two stages to estimate the depth. First is the initial estimation base on the epipolar plane images (EPIs). Second is the refinement of the estimations. At the initial estimation, adaptive window matching is used to improve t...

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Bibliographic Details
Published in:Journal of imaging 2017-06, Vol.3 (2), p.17
Main Authors: Lin, Pei-Hsuan, Yeh, Jeng-Sheng, Wu, Fu-Che, Chuang, Yung-Yu
Format: Article
Language:English
Subjects:
Algorithms
depth estimation
Digital cameras
EPI
International conferences
Light
light fields
Lytro
Markov processes
Matching
Patches (structures)
Pattern recognition
Photography
Sensors
Software
Texture
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Summary:A depth estimation algorithm from plenoptic images is presented. There are two stages to estimate the depth. First is the initial estimation base on the epipolar plane images (EPIs). Second is the refinement of the estimations. At the initial estimation, adaptive window matching is used to improve the robustness. The size of the matching window is based on the texture description of the sample patch. Based on the texture entropy, a smaller window is used for a fine texture. A smooth texture requires a larger window. With the adaptive window size, different reference patches based on various depth are constructed. Then the depth estimation compares the similarity among those patches to find the best matching patch. To improve the initial estimation, a refinement algorithm based on the Markov Random Field (MRF) optimization is used. An energy function keeps the data similar to the original estimation, and then the data are smoothed by minimizing the second derivative. Depth values should satisfy consistency across multiple views.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging3020017