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Single image dehazing is an ill-posed problem that has recently drawnimportant attention. Despite the significant increase in interest shown fordehazing over the past few years, the validation of the dehazing methodsremains largely unsatisfactory, due to the lack of pairs of real hazy andcorresponding haze-free reference images. To address this limitation, weintroduce Dense-Haze - a novel dehazing dataset. Characterized by dense andhomogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy andcorresponding haze-free images of various outdoor scenes. The hazy scenes havebeen recorded by introducing real haze, generated by professional hazemachines. The hazy and haze-free corresponding scenes contain the same visualcontent captured under the same illumination parameters. Dense-Haze datasetaims to push significantly the state-of-the-art in single-image dehazing bypromoting robust methods for real and various hazy scenes. We also provide acomprehensive qualitative and quantitative evaluation of state-of-the-artsingle image dehazing techniques based on the Dense-Haze dataset. Notsurprisingly, our study reveals that the existing dehazing techniques performpoorly for dense homogeneous hazy scenes and that there is still much room forimprovement.

Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images