See Through the Windshield from Surveillance Camera

Daiqian Ma, Yan Bai, Renjie Wan, Ce Wang, Boxin Shi, Ling-Yu Duan

Abstract: This paper attempts to address the challenging task of seeing through the windshield images captured by surveillance cameras in the wild. Such images usually have very low visibility due to heterogeneous degradations caused by blur, haze, reflection, noise etc., which makes existing image enhancing methods inapplicable. We propose a windshield image restoration generative adversarial network (WIRE-GAN) to restore and enhance the visibility of windshield images. We adopt the weakly supervised framework based on the generative model, which has effectively released the request of paired training data for a specific type of degradation. To generate more semantically consistent results even in extreme lighting conditions, we introduce a novel content-preserving strategy into the proposed weakly-supervised framework. To make the image restoration more reliable, the WIRE-GAN network constructs a sort of content-aware embedding space and enforces the constraint of the restored windshield images being closer to the original input in the embedding space. Moreover, we collect a large-scale windshield image dataset (WIRE dataset) to validate the advantage of our method in improving the image quality, and further evaluate the impact of windshield restoration on the vehicle ReID performance.