HSV-3S and 2D-GDA for High-Saturation Low-Light Image Enhancement in Night Traffic Monitoring

This paper proposes HSV (hue, saturation, value) with three sectors (HSV-3S) and two-dimensional gradient descent algorithm (2D-GDA) for high-saturation low-light image enhancement in night traffic monitoring (NTM). The saturation of HSV-3S is defined as the ratio of the projection vector length and twice length of the sector start vector, which results in that the saturation of HSV-3S is smaller than that of HSV, and a saturation weakening model is proposed to further decrease the saturation of HSV-3S. The hue of HSV-3S is defined as the cosine value of the included angle between the projection vector and the sector start vector in each of three sectors. HSV-3S is more concise and faster than HSV. Then, 2D-GDA extends the gradient descent algorithm to 2D image domain. 2D-GDA employs the iteration matrix with variable step values (i.e., the step values of the dark regions are less than those of the bright regions), which can improve the pixel distribution of the 2D-GDA enhanced image. Finally, the HSV-3S+2D- GDA based RGB image can be obtained by performing 2D-GDA on the value of HSV-3S with transforming the processed HSV-3S to RGB. The experimental results on NTM (i.e., the brevity name of the database collected from real ITS), LOL, ExDark and SICE databases, indicate that HSV-3S+2D-GDA is fast and efficient for high-saturation low-light image enhancement.