Low-light Image Enhancement Using Dual Convolutional Neural Networks for Vehicular Imaging Systems

Eunjae Ha, Heunseung Lim, Soohwan Yu, Joonki Paik

Abstract: This paper presents a low-light image enhancement method using a convolutional neural network (CNN). Given a lowlight input image, the proposed method converts RGB color space to CIELAB color space. The luminance and chrominance components are separately enhanced. The luminance channel is enhanced using a CNN to enhance the brightness. On the other hand, the chrominance channels are enhanced using a dilated CNN to reduce the color distortion. Experimental results demonstrate that the proposed method can successfully enhance low-light images of a vehicular imaging system without color distortion.