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Example-based learning using heuristic orthogonal matching pursuit teaching mechanism with auxiliary coefficient representation for the problem of de-fencing and its affiliated applications

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Abstract

Orthogonal Matching Pursuit (OMP) is a good candidate for solving energy function optimization problems. In this paper, we propose a novel auxiliary coefficient representation for the problem of image de-fencing. To improve the optimization efficiency of the OMP algorithm, we propose a heuristic form of the OMP (named h-OMP) approximation based on auxiliary coefficient representation. A frequency-domain optimization approach is derived by selecting an over-complete example set for the image signal, the h-OMP algorithm is used to simultaneously remove the fences on the image matrix and find the auxiliary coefficient basis to form the image segment. Experiments show that the proposed h-OMP algorithm generates better output image, whose performance is superior in terms of both subjective and objective evaluation criteria.

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Notes

  1. The term “fence” here just means physically literal fence, such as shown in Figure 4.

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Acknowledgments

Funding for this work was supported by the project of Shanghai Universities Young Teacher Training Scheme under Grant No. ZZSB17004.

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  1. Shanghai Sipo Polytechnic, 1408 Chengnan Road, Pudong District, Shanghai, 201300, China

    Min Li

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  1. Min Li
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Correspondence to Min Li.

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Li, M. Example-based learning using heuristic orthogonal matching pursuit teaching mechanism with auxiliary coefficient representation for the problem of de-fencing and its affiliated applications. Appl Intell 48, 2884–2893 (2018). https://doi.org/10.1007/s10489-017-1079-9

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