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Accurate image reconstruction by separable krawtchouk-charlier moments with automatic parameter selection using artificial bee colony optimization

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Abstract

The feature extraction process involves identifying and extracting relevant features from specific data. However, in most cases, this data can be a set of images. In fact, one of the most commonly used techniques for image analysis are orthogonal moments. This paper introduces a new 2-dimensional separable moment named optimized Krawtchouk-Charlier moments for image analysis. It should be noted that Krawtchouk and Charlier polynomials are parametric depends on p and \(a_1\)parameters respectively. Which means that the values of these parameters can directly affect image representation and reconstruction. For this reason, parameter selection for this kind of orthogonal moments is a major issue that impedes the efficiency of modern orthogonal moment applications. So as to solve this problem, we propose a method that uses an artificial bee colony optimization algorithm. Selecting automatically the appropriate parameters for a specific order of moments and enhancing the presentation and reconstruction of digital images. Hence, proposed method is based on optimizing the normalized mean square reconstruction error. The performance of our method is tested using the reconstruction error of binary, gray-scale, and color images with varying moments’ order using separable discrete Krawtchouk-Charlier moments. Therefore, based on obtained results. This method offers optimal image reconstruction compared to manually selected parameters. In addition, this method gives great results when the image is translated or affected by noise. Where the optimization algorithm gives the best parameters p and \(a_1\), which makes the moment focus on the most important pixels in the image.

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Authors and Affiliations

  1. Laboratory of Innovation in Mathematics and Application in Information Technologies, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Abdelati Bourzik & Belaid Bouikhalene

  2. Innovative Technologies Laboratory, Superior School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Jaouad El-Mekkaoui

  3. Laboratory of Information, Signals, Automation and Cognitivism, Faculty of Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Amal Hjouji

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  1. Abdelati Bourzik
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Bourzik, A., Bouikhalene, B., El-Mekkaoui, J. et al. Accurate image reconstruction by separable krawtchouk-charlier moments with automatic parameter selection using artificial bee colony optimization. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19664-0

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