Low-Complexity Deep HDR Fusion And Tone Mapping for Urban Traffic Scenes

In this paper we propose a computationally efficient neural network for high dynamic range fusion and tone mapping, for application in perception systems of autonomous vehicles. The proposed approach fuses two consecutive, differently exposed images into a single output with good exposure in all regions, in a standard dynamic range. Motion is compensated based on fast optical flow estimation, and subsequently by including an error mask as an input to the network to indicate the remaining artifact-prone regions. This is an efficient way for the network to learn to reduce the ghosting artifacts without increasing computational complexity. Unlike the conventional approach, we train the network on versatile traffic data, and evaluate the performance based on object detection quality metrics, rather than for visual quality. The performance was compared to a similarly complex representative method from literature. We achieved improved performance in challenging light conditions due to the robustness of our method in variable traffic conditions.