Variational Disparity Estimation Framework for Plenoptic Image

TL;DR

This paper introduces a variational framework for precise disparity estimation in plenoptic images, incorporating a light-field motion tensor, warping strategy, and filtering to improve accuracy, especially in occluded regions.

Contribution

The novel framework combines variational principles with a light-field motion tensor and advanced regularization to enhance disparity estimation accuracy in plenoptic images.

Findings

Outperforms Lytro software and recent methods on synthetic datasets

Achieves sub-pixel disparity accuracy with robust data terms

Effectively handles large displacements and occlusions

Abstract

This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping strategy is embedded in our framework for tackling the large displacement problem. We also show that by applying a simple regularization term and a guided median filtering, the accuracy of displacement field at occluded area could be greatly enhanced. We demonstrate the excellent performance of the proposed framework by intensive comparisons with the Lytro software and contemporary approaches on both synthetic and real-world datasets.