Explicit Edge Inconsistency Evaluation Model for Color-guided Depth Map Enhancement

Institute of Electrical and Electronics Engineers
Publication Type:
Journal Article
IEEE Transactions on Circuits and Systems for Video Technology, 2018
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
07567493.pdfAccepted Manuscript Version15.86 MB
Adobe PDF
Color-guided depth enhancement is to refine depth maps according to the assumption that the depth edges and the color edges at the corresponding locations are consistent. In the methods on such low-level vision task, Markov Random Fields (MRF) including its variants is one of major approaches, which has dominated this area for several years. However, the assumption above is not always true. To tackle the problem, the state-of-the-art solutions are to adjust the weighting coefficient inside the smoothness term of MRF model. These methods are lack of explicit evaluation model to quantitatively measure the inconsistency between the depth edge map and the color edge map, so it cannot adaptively control the efforts of the guidance from the color image for depth enhancement leading to various defects such as texture-copy artifacts and blurring depth edges. In this paper, we propose a quantitative measurement on such inconsistency and explicitly embed it into the smoothness term. The proposed method demonstrates the promising experimental results when compared with benchmark and the state-of-the-art methods on Middlebury datasets, ToF-Mark datasets and NYU datasets.
Please use this identifier to cite or link to this item: