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Two low illuminance image enhancement algorithms based on grey level mapping

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Abstract

Two image enhancement contrast methods are proposed in this paper for low-intensity images. The first method (LEAM) is a new greyscale mapping function, and it can be significantly enhanced in the low grey range and compressed slowly in the high grey range, which is beneficial for retaining more image details; the second method (LEAAM) is based on the data characteristics of a histogram combined with the first mapping function, which adaptively sets the gamma value to correct the image. The experimental results show that compared with a traditional mapping function, LEAM is more effective at enriching image details and enhancing visual effects, and LEAAM, compared with a recent low-illumination image enhancement algorithm, achieves good performance for average gradient, information entropy and contrast index; additionally, the overall visual effect is the best compared with other methods.

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Funding

The financial supports by the Ministry of human resources and social security (198606), Science and Technology Department of Sichuan Province (2020JDRC0026), as well as the special fund for central finance of universities (2018SCU12065).

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  1. College of Mechanical Engineering, Sichuan University, Chengdu, China

    Hong Cheng, Wei Long, Yanyan Li & Huaguo Liu

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  1. Hong Cheng
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  2. Wei Long
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Correspondence to Hong Cheng.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Two low illuminance image enhancement algorithms based on grey level mapping”.

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Cheng, H., Long, W., Li, Y. et al. Two low illuminance image enhancement algorithms based on grey level mapping. Multimed Tools Appl 80, 7205–7228 (2021). https://doi.org/10.1007/s11042-020-09919-x

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  • DOI: https://doi.org/10.1007/s11042-020-09919-x

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