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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >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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Example-based learning using heuristic orthogonal matching pursuit teaching mechanism with auxiliary coefficient representation for the problem of de-fencing and its affiliated applications

机译：基于示例的基于学习，使用启发式正交匹配追求教学机制，辅助系数表示脱离围栏问题及其附属应用

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摘要

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.
机译：正交匹配追求（OMP）是解决能量函数优化问题的良好候选者。 在本文中，我们提出了一种新的辅助系数表示，用于图像去围栏的问题。 为了提高OMP算法的优化效率，我们提出了一种基于辅助系数表示的OMP（命名H-OMP）近似的启发式形式。 通过选择用于图像信号的完整示例设置来导出频域优化方法，使用H-OMP算法在图像矩阵上同时移除栅栏，并找到辅助系数以形成图像段。 实验表明，所提出的H-OMP算法产生更好的输出图像，其性能在主观和客观评估标准方面优越。

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