Low-Light Image Enhancement for Autonomous Driving Systems using DriveRetinex-Net
Long Hoang Pham, Duong Nguyen‐Ngoc Tran, Jae Wook Jeon
Abstract
Most autonomous driving algorithms are designed for normal-light images. Hence, insufficient lighting during image capture significantly degrades the visibility of images and hurts the performance of many computer vision systems. Retinex theory is an effective tool for enhancing the illumination and detail of images. In this paper, we collected a Low-Light Drive (LOL-Drive) dataset and applied a deep retinex neural network, named DriveRetinex, which was taught using this dataset. The deep Retinex-Net consists of two subnetworks: Decom-Net (decomposes a color image into a reflectance map and an illumination map) and Enhance-Net (enhances the light level in the illumination map). The whole architecture can be trained in an end-to-end fashion. Extensive experiments demonstrate that the proposed method not only achieves visually appealing low-light enhancement, but it also increases the accuracy of object detection in autonomous driving systems.