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An Efficient Stereo Matching Algorithm Based on Four-Moded Census Transform for High-Resolution Images

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3D Research

Abstract

By establishing slanted surfaces, PatchMatch stereo (PMS) algorithm can achieve impressive disparity details and high sub-pixel precision. However, it is too unwieldy for practical calculations in handling images with high resolution. In this paper, we improved the PMS algorithm to efficiently handle the high-resolution images. Firstly, four-mode census transform, which can improve matching accuracy and solve the problem of the center pixel distortion effectively, is applied to measure the dissimilarity between pixels, instead of the absolute differences of the gray-value and the gray-value gradient. Utilizing this transform can halve the time of dissimilarity measurement compared to that of PMS. Then, the proposed algorithm adopt the integer disparity plane approximation strategy during the PatchMatch inference procedure. This strategy is applied in the random initialization step, the computation of the matching cost and the process of searching and renewing the minimum matching cost. Finally, the outlier pixels are refined with the post-process steps. Experimental results show that the proposed algorithm is more efficient than the PMS algorithm and generates comparable disparity maps in handling the high-resolution images.

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Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 61401137, 61404043), Key Science and Technology Project of Anhui Province (Grant No. 16030901007).

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Authors and Affiliations

  1. School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China

    Yizhong Yang, Dong Xu, Shen Rong, Guangjun Xie & Feng Chen

Authors
  1. Yizhong Yang
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  2. Dong Xu
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  3. Shen Rong
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  4. Guangjun Xie
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  5. Feng Chen
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Corresponding author

Correspondence to Yizhong Yang.

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Yang, Y., Xu, D., Rong, S. et al. An Efficient Stereo Matching Algorithm Based on Four-Moded Census Transform for High-Resolution Images. 3D Res 9, 33 (2018). https://doi.org/10.1007/s13319-018-0185-8

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  • DOI: https://doi.org/10.1007/s13319-018-0185-8

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