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Disparity map optimization using sparse gradient measurement under intensity-edge constraints

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Abstract

Common local stereo methods often compute integer-valued disparities at support windows. The implicit assumption of a constant disparity value in support windows generally does not produce accurate results on slanted surfaces. In this paper, we propose a global optimization method for reconstructing disparity maps. Our optimization strategy is an extension of Xu’s (ACM Trans Graph 30(6):1–12, 2011) image smoothing algorithm. The strategy is to develop a sparse gradients counting model of the disparity map, coupled with the priors of intensity edges of the reference image. Based on this model, the disparity optimization problem is then formulated as a constrained optimization objective function, which is finally solved via the half-quadratic splitting algorithm. Experimental results demonstrated that the proposed approach improves the quality of the disparity maps regarding slanted surfaces and local discontinuity preservation.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61263046, 61462065), Natural Science Foundation of Jiangxi Province (20122BAB201037).

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Authors and Affiliations

  1. School of Mechanical & Electrical Engineering, Nanchang University, Nanchang, China

    Jun Miao

  2. Institute of Computer Vision, Nanchang Hangkong University, Nanchang, China

    Jun Miao, Jun Chu & Guimei Zhang

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  1. Jun Miao
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  2. Jun Chu
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  3. Guimei Zhang
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Correspondence to Jun Chu.

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Miao, J., Chu, J. & Zhang, G. Disparity map optimization using sparse gradient measurement under intensity-edge constraints. SIViP 10, 161–169 (2016). https://doi.org/10.1007/s11760-014-0722-8

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  • DOI: https://doi.org/10.1007/s11760-014-0722-8

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