Variational Light Field Analysis for Disparity Estimation and Super-Resolution
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Abstract
We develop a continuous framework for the analysis of 4D light fields, and describe novel variational methods for disparity reconstruction as well as spatial and angular super-resolution. Disparity maps are estimated locally using epipolar plane image analysis without the need for expensive matching cost minimization. The method works fast and with inherent subpixel accuracy since no discretization of the disparity space is necessary. In a variational framework, we employ the disparity maps to generate super-resolved novel views of a scene, which corresponds to increasing the sampling rate of the 4D light field in spatial as well as angular direction. In contrast to previous work, we formulate the problem of…
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Topics
Keywords
- Subpixel rendering
- Epipolar geometry
- Computer science
- Computer vision
- Artificial intelligence
- Iterative reconstruction
- Algorithm
- Light field
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