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Neural image reconstruction using a heuristic validation mechanism

  • S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
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Abstract

Image reconstruction is a mathematical process, where the image is compressed into a small representation and derived from this form. The general use of the reconstruction technique finds a place in noise removal from images obtained in medicine or other areas of life. In this paper, we propose a heuristic validation mechanism for training different types of neural networks in the problem of image reconstruction. The main idea is based on finding some important areas on image by heuristic algorithm and train network until a certain level of entropy of these areas is achieved. The mathematical model of this technique is described and supported by experimental results on different datasets with complex analysis of different heuristics. Proposed approach shows that it can reduce the average time of training process using convolutional neural networks.

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Acknowledgements

This work was funded by the National Centre for Research and Development of Poland (0080/DIA/2016/45) held by Dawid Polap. This research was partially funded by the Natural Sciences and Engineering Council of Canada (NSERC) Discovery Grant held by Gautam Srivastava.

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Authors and Affiliations

  1. Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100, Gliwice, Poland

    Dawid Połap

  2. Department of Mathematics and Computer Science, Brandon University, Brandon, MB, R7A 6A9, Canada

    Gautam Srivastava

  3. Research Centre of Interneural Computing, China Medical University, Taichung, Taiwan

    Gautam Srivastava

Authors
  1. Dawid Połap
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  2. Gautam Srivastava
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Correspondence to Dawid Połap.

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Authors acknowledge the contribution to this project of the “Diamond Grant 2016” No. 0080/DIA/2016/45 from the Polish Ministry of Science and Higher Education.

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Połap, D., Srivastava, G. Neural image reconstruction using a heuristic validation mechanism. Neural Comput & Applic 33, 10787–10797 (2021). https://doi.org/10.1007/s00521-020-05046-8

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  • DOI: https://doi.org/10.1007/s00521-020-05046-8

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