Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years, the validation of the dehazing methods remains largely unsatisfactory, due to the lack of pairs of real hazy and corresponding haze-free reference images. To address this limitation, we introduce Dense-Haze – a novel dehazing dataset. Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes. The hazy scenes have been recorded by introducing real haze, generated by professional haze machines. The hazy and haze-free corresponding scenes contain the same visual content captured under the same illumination parameters. Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes. We also provide a comprehensive qualitative and quantitative evaluation of state-of-the-art single image dehazing techniques based on the Dense-Haze dataset. Not surprisingly, our study (using traditional image quality metrics such as PSNR, SSIM and CIEDE2000) reveals that the existing dehazing techniques perform poorly for dense homogeneous hazy scenes and that there is still much room for improvement.
NTIRE is a CVPR workshop that aims to provide an overview of the new trends and advances in those areas. Moreover, it offers an opportunity for academic and industrial attendees to interact and explore collaborations. Jointly with workshop NTIRE organised in 2019 the second image dehazing online challenge.
Dense-Haze has been employed in the dehazing challenge of the NTIRE 2019 CVPR workshop.
DENSE-HAZE: A BENCHMARK FOR IMAGE DEHAZING WITH DENSE-HAZE AND HAZE-FREE IMAGES