HazeRD: An outdoor scene dataset and benchmark for single image dehazing

• Published in: 2017 IEEE International Conference on Image Processing (ICIP) pp. 3205 - 3209
• Main Authors: Zhang, Yanfu, Ding, Li, Sharma, Gaurav
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Atmospheric modeling; benchmark; Benchmark testing; Cameras; dataset; dehazing; depth; Image color analysis; Meteorology; Scattering; Visualization
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2017.8296874

In this paper, a new dataset, HazeRD, is proposed for benchmarking dehazing algorithms under more realistic haze conditions. HazeRD contains fifteen real outdoor scenes, for each of which five different weather conditions are simulated. As opposed to prior datasets that made use of synthetically generated images or indoor images with unrealistic parameters for haze simulation, our outdoor dataset allows for more realistic simulation of haze with parameters that are physically realistic and justified by scattering theory. All images are of high resolution, typically six to eight megapixels. We test the performance of several state-of-the-art dehazing techniques on HazeRD. The results exhibit a significant difference among algorithms across the different datasets, reiterating the need for more realistic datasets such as ours and for more careful benchmarking of the methods.