MRF-Based Disparity Upsampling Using Stereo Confidence Evaluations

Abstract

Disparity upsampling methods are derived for restoring high-quality disparity maps from active three-dimensional imaging techniques such as time-of-flight method. Although effective, traditional upsampling methods exhibit certain drawbacks. For example, noisy disparity values in the low-scaled level are taken indiscriminately to carry out the upsampling assignment, which could lead to dramatically weakened results. To solve this problem, we herein present a Markov random field (MRF) based disparity upsampling method using confidence evaluations. We utilize confidence measures under the stereo configuration to evaluate the reliability of disparity value. The confidence maps are then properly integrated into the state-of-the-art MRF-based depth upsampling (MBU) method to constrain the negative effect caused by the noisy disparity. More specifically, the confidence is used to remove unqualified pixels from participating in the disparity upsampling computation. Extensive experiments were performed to validate the proposed new method. In these experiments, initial low-scaled disparity maps were from either ground truth or stereo matching methods. Results verified that our method can obtain significantly better upsampled disparity maps than that from the original MBU or other nonconfidence-based methods. In addition, we implemented our algorithms on general public utilities to improve the execution speed.