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

Shouxin Liu, Wei Long, Yanyan Li, Hong Cheng

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.