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Low-light image enhancement based on membership function and gamma correction

  • 1200: Machine Vision Theory and Applications for Cyber Physical Systems
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Abstract

The aim of low-light image enhancement algorithms is to improve the luminance of images. However, existing low-light image enhancement algorithms inevitably cause an enhanced image to be over- or underenhanced and cause color distortion, both of which prevent the enhanced images from obtaining satisfactory visual effects. In this paper, we proposed a simple but effective low-light image enhancement algorithm based on a membership function and gamma correction (MFGC). First, we convert the image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and design a method to achieve the self-adaptation computation of traditional membership function parameters. Then, we use the results of the membership function as the γ value and adjust coefficient c of the gamma function based on the characteristics of different images with different gray levels. Finally, we design a linear function to avoid underenhancement. The experimental results show that our method not only has lower computational complexity but also greatly improves the brightness of low-light areas and addresses uneven brightness. The images enhanced using the proposed method have better objective and subjective image quality evaluation results than other state-of-the-art methods.

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Acknowledgements

This work was supported by Science and Technology Department of Sichuan Province, People’s Republic of China (No. 2020JDRC0026).

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Authors and Affiliations

  1. School of Mechanical Engineering, Sichuan University, Chengdu, People’s Republic of China

    Shouxin Liu, Wei Long, Yanyan Li & Hong Cheng

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  1. Shouxin Liu
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  2. Wei Long
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  3. Yanyan Li
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  4. Hong Cheng
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Correspondence to Shouxin Liu.

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the manuscript entitled “Low-Light Image Enhancement Based on Membership Function and Gamma Correction”.

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Liu, S., Long, W., Li, Y. et al. Low-light image enhancement based on membership function and gamma correction. Multimed Tools Appl 81, 22087–22109 (2022). https://doi.org/10.1007/s11042-021-11505-8

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  • DOI: https://doi.org/10.1007/s11042-021-11505-8

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