Depth Estimation for Lytro Images by Adaptive Window Matching on EPI
AbstractA 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
Lin P-H, Yeh J-S, Wu F-C, Chuang Y-Y. Depth Estimation for Lytro Images by Adaptive Window Matching on EPI. Journal of Imaging. 2017; 3(2):17.Chicago/Turabian Style
Lin, Pei-Hsuan; Yeh, Jeng-Sheng; Wu, Fu-Che; Chuang, Yung-Yu. 2017. "Depth Estimation for Lytro Images by Adaptive Window Matching on EPI." J. Imaging 3, no. 2: 17.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.