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The changing level of haze is one of the main factors which affects thesuccess of the proposed dehazing methods. However, there is a lack ofcontrolled multi-level hazy dataset in the literature. Therefore, in thisstudy, a new multi-level hazy color image dataset is presented. Color videodata is captured for two real scenes with a controlled level of haze. Thedistance of the scene objects from the camera, haze level, and ground truth(clear image) are available so that different dehazing methods and models canbe benchmarked. In this study, the dehazing performance of five differentdehazing methods/models is compared on the dataset based on SSIM, PSNR, VSI andDISTS image quality metrics. Results show that traditional methods cangeneralize the dehazing problem better than many deep learning based methods.The performance of deep models depends mostly on the scene and is generallypoor on cross-dataset dehazing.

A New Multi-Level Hazy Image and Video Dataset for Benchmark of Dehazing Methods