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Analysis of Different Image Enhancement and Feature Extraction Methods

Author

Listed:
  • Lucero Verónica Lozano-Vázquez

    (Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional—ESIME Zacatenco, Mexico City 07738, Mexico
    These authors contributed equally to this work.)

  • Jun Miura

    (LINCE Lab, Toyohashi University of Technology, Toyohashi 441-8580, Japan
    These authors contributed equally to this work.)

  • Alberto Jorge Rosales-Silva

    (Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional—ESIME Zacatenco, Mexico City 07738, Mexico
    These authors contributed equally to this work.)

  • Alberto Luviano-Juárez

    (Instituto Politécnico Nacional—UPIITA, Mexico City 07340, Mexico
    These authors contributed equally to this work.)

  • Dante Mújica-Vargas

    (Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca 62490, Mexico
    These authors contributed equally to this work.)

Abstract

This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.

Suggested Citation

  • Lucero Verónica Lozano-Vázquez & Jun Miura & Alberto Jorge Rosales-Silva & Alberto Luviano-Juárez & Dante Mújica-Vargas, 2022. "Analysis of Different Image Enhancement and Feature Extraction Methods," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2407-:d:859276
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    Citations

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    Cited by:

    1. Chenping Zhao & Wenlong Yue & Jianlou Xu & Huazhu Chen, 2023. "Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
    2. Hancheng Zhu & Yong Zhou & Zhiwen Shao & Wenliang Du & Guangcheng Wang & Qiaoyue Li, 2022. "Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning," Mathematics, MDPI, vol. 10(22), pages 1-15, November.

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