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Brightness-controlled enhancement for soil image based on conic curve

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Abstract

Soil images collected under different illumination conditions can be enhanced with a brightness-controlled enhancement algorithm, so that the result image is similar to the real image of the same soil photographed under specific natural light. It can improve the accuracy of soil species identification with machine vision because the image features of distinct soil species are described with a uniform brightness benchmark. To address it, a nonlinear mapping algorithm based on conic curve is proposed to realize the brightness-controlled enhancement of soil image. Firstly, a weighted quadric curve is derived in theory to reconstruct the brightness mapping according to the brightness relation between a pair of soil images with different brightness. Then, the brightness transformation range and the bending degree of brightness mapping curve are searched by the idea of binary search and gradual approximation to meet the given target brightness. The enhanced image is finally obtained by weighting and fusing the results of curve mapping in RGB space and HSV space. Extensive experiment results demonstrate that the proposed method is superior to comparison methods for brightness migration. Our algorithm can improve the accuracy of CNN models by up to 4.94%. Subjective evaluation shows that the effective range of soil image brightness enhancement with the proposed method is plus or minus 30 gray levels of brightness. The simulation result proves its effectiveness in brightness-controlled enhancement of soil image.

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Code Availability underlying the results presented in this paper is not publicly available at this time but may be obtained from the authors upon reasonable request.

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Acknowledgements

This work supported by Key Science and Technology Research Program (No. KJZD-K201900505) of Chongqing, China; Chongqing University Innovation Research Group funding (No. CXQT20015) of Chongqing Municipal Education Commission, China; Technology Foresight and Institutional Innovation (CSTB2022TFII-OFX0043) of Chongqing, China; Graduate Student Research and Innovation Program (CYS22564) of Chongqing, China.

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Authors and Affiliations

  1. College of Computer and Information Science, Chongqing Normal University, No. 37 at Middle Road of University Town, Shapingba, 401331, Chongqing, China

    Shaohua Zeng, Weiran Wu & Yan Xia

  2. Chongqing Center of Engineering Technology Research on Digital Agricultural Service, No. 37 at Middle Road of University Town, Shapingba, 401331, Chongqing, China

    Shaohua Zeng, Weiran Wu & Yan Xia

  3. Chongqing Master Station of Agricultural Technology Promotion, No. 186 at East Huangshan Avenue, New North Zone, 401121, Chongqing, China

    Shuai Wang

  4. Tibet Academy of Agricultural and Animal Husbandry Sciences, No. 130 at West Jinzhu Road, Lhasa, 850032, Tibet, China

    Guoyi Liu

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  1. Shaohua Zeng
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  2. Weiran Wu
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  4. Shuai Wang
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  5. Guoyi Liu
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Contributions

SZ and WW wrote the main manuscript text. YX provides many useful advices. SW and GL provide the dataset. All authors reviewed the manuscript

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Correspondence to Shaohua Zeng.

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Zeng, S., Wu, W., Xia, Y. et al. Brightness-controlled enhancement for soil image based on conic curve. SIViP 18, 1493–1506 (2024). https://doi.org/10.1007/s11760-023-02858-8

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  • DOI: https://doi.org/10.1007/s11760-023-02858-8

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