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Low illumination color image enhancement based on Gabor filtering and Retinex theory

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Abstract

Aiming at the shortcomings of traditional Retinex image enhancement algorithms, such as poor texture detail retention, halo, over-enhancement and hue mutation, a low-illuminance color image enhancement algorithm based on Gabor filter and Retinex theory is proposed. The algorithm extracts the luminance I component from the HSI color space of the original image, and then performs MSRCR (Retinex algorithm for color restoration) enhancement on the luminance I component to obtain an enhanced luminance I component and color reproduction image. On the other hand, the original image is enhanced by a SSR (Single Scale Retinex Algorithm) based on the Gabor filter in the RGB color space to obtain an enhanced image with better texture and edge details. Then, the two images enhanced in two different ways are weighted and merged to obtain the final enhanced image. This algorithm is compared with the SSR algorithm based on Gamma correction, the MSR (multi-scale Retinex algorithm) based on bilateral filtering and the improved MSRCR algorithm. Taking mean square error, information entropy, and average gradient as evaluation indicators, the experimental results show that the image information processed by this algorithm is rich in color, rich in color, and color is closer to the original image, effectively reducing the occurrence of halo and excessive enhancement. This algorithm has certain reference significance for the enhancement of low-light color images.

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Acknowledgments

This research was funded by the National Natural Science Foundation of China, grant number 6192007, 61462008, 61751213, 61866004; the Key projects of Guangxi Natural Science Foundation, grant number 2018GXNSFDA294001,2018GXNSFDA281009; the Natural Science Foundation of Guangxi, grant number 2018GXNSFAA294050, 2017GXNSFAA198365; 2015 Innovation Team Project of Guangxi University of Science and Technology, grant number gxkjdx201504; Innovation Project for College Students of Guangxi University of Science and Technology, grant number GKYC201708; Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security, grant number MIMS19-04.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Guangxi University of Science and Technology, Guangxi, Liuzhou, 545000, China

    Ping Wang & Dong Lv

  2. College of Computer Science and Communication Engineering, Guangxi University of Science and Technology, Guangxi, Liuzhou, 545000, China

    Zhiwen Wang

  3. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541000, China

    Zhiwen Wang, Chanlong Zhang & Yuhang Wang

Authors
  1. Ping Wang
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  2. Zhiwen Wang
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  3. Dong Lv
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  4. Chanlong Zhang
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  5. Yuhang Wang
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Contributions

Conceptualization, P.W. and D.L.; methodology, P.W.; software, Y.h.W.; validation, P.W., D.L.; formal analysis, P.W.; investigation, P.W.; resources, C.l.Z.; data curation, Y.h.W.; writing—original draft preparation, P.W.; writing—review and editing, Z.w.W.; visualization, P.W.; supervision, C.L.Z.; project administration, Z.w.W.; funding acquisition, Z.w.W. All authors have read and agreed to the published version of the manuscript.”

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Correspondence to Zhiwen Wang.

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Wang, P., Wang, Z., Lv, D. et al. Low illumination color image enhancement based on Gabor filtering and Retinex theory. Multimed Tools Appl 80, 17705–17719 (2021). https://doi.org/10.1007/s11042-021-10607-7

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  • DOI: https://doi.org/10.1007/s11042-021-10607-7

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