Nighttime Dehazing with a Synthetic Benchmark
This addresses the challenge of nighttime image dehazing for computer vision applications, but it is incremental as it builds on existing methods with a new dataset and specific techniques.
The authors tackled the problem of improving visibility in nighttime hazy images by proposing a synthetic benchmark generation method (3R) and a dehazing approach using an optimal-scale maximum reflectance prior and a learning-based baseline, achieving superior image quality and runtime compared to state-of-the-art methods.
Increasing the visibility of nighttime hazy images is challenging because of uneven illumination from active artificial light sources and haze absorbing/scattering. The absence of large-scale benchmark datasets hampers progress in this area. To address this issue, we propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images, which first reconstructs the scene geometry, then simulates the light rays and object reflectance, and finally renders the haze effects. Based on it, we generate realistic nighttime hazy images by sampling real-world light colors from a prior empirical distribution. Experiments on the synthetic benchmark show that the degrading factors jointly reduce the image quality. To address this issue, we propose an optimal-scale maximum reflectance prior to disentangle the color correction from haze removal and address them sequentially. Besides, we also devise a simple but effective learning-based baseline which has an encoder-decoder structure based on the MobileNet-v2 backbone. Experiment results demonstrate their superiority over state-of-the-art methods in terms of both image quality and runtime. Both the dataset and source code will be available at https://github.com/chaimi2013/3R.