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J. Imaging 2017, 3(2), 17; doi:10.3390/jimaging3020017

Depth Estimation for Lytro Images by Adaptive Window Matching on EPI

1
Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
2
Computer and Communication Engineering, Ming Chuan University, Taoyuan County 33348, Taiwan
3
Computer Science and Communication Engineering, Providence University, Taichung City 43301, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 13 December 2016 / Revised: 23 April 2017 / Accepted: 11 May 2017 / Published: 21 May 2017
(This article belongs to the Special Issue 3D Imaging)
View Full-Text   |   Download PDF [6468 KB, uploaded 21 May 2017]   |  

Abstract

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. View Full-Text
Keywords: light fields; depth estimation; EPI; Lytro light fields; depth estimation; EPI; Lytro
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lin, P.-H.; Yeh, J.-S.; Wu, F.-C.; Chuang, Y.-Y. Depth Estimation for Lytro Images by Adaptive Window Matching on EPI. J. Imaging 2017, 3, 17.

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