Self-supervised network for low-light traffic image enhancement based on deep noise and artifacts removal

Abstract

In the intelligent transportation system (ITS), detecting vehicles and pedestrians in low-light conditions is challenging due to the low contrast between objects and the background. Recently, many works have enhanced low-light images using deep learning-based methods, but these methods require paired images during training, which are impractical to obtain in real-world traffic scenarios. Therefore, we propose a self-supervised network (SSN) for low-light traffic image enhancement that can be trained without paired images. To avoid amplifying noise and artifacts in the processed image during enhancement, we first proposed a denoising net to reduce the noise and artifacts in the input image. Then the processed image can be enhanced by the enhancement net. Considering the compression of the traffic image, we designed an artifacts removal net to improve the quality of the enhanced image. We proposed several effective and differential losses to make SSN trainable with low-light images only. To better integrate the extracted features from different levels in the network, we also proposed an attention module named the multi-head non-local block. In experiments, we evaluated SSN and other low-light image enhancement methods on two low-light traffic image sets: the Berkeley Deep Drive (BDD) dataset and the Hong Kong night-time multi-class vehicle (HK) dataset. The results indicated that SSN significantly improves upon other methods in visual comparison and some blind image quality metrics. We also conducted comparisons on classical ITS tasks like vehicle detection on the images enhanced by SSN and other methods, which further verified its effectiveness.