Image Dehazing Guided by Low-Pass Reinforced Airlight

We introduce a simple but robust method to restore the visibility of hazy images. Our non deep-learning strategy refines a simplistic approximation of the airlight by taking advantage of the O-HAZE dataset that contains also the corresponding haze-free images. Knowing that the transmission is generally characterized by small values in hazy scenes, based on the optical model, we first assumes zero transmission, and approximate the airlight by the input hazy image. Then, using hazy and corresponding haze-free images available from the O-HAZE dataset, a second airlight estimate can be computed by solving the optical model assuming a simplified transmission map derived from dark channel prior. Observing that the difference between these two airlight estimates, primarily contains the low frequencies of the hazy image, we refine the airlight approximation derived from a zero transmission by reinforcing its low frequency component. We extensively tested our approach on real world hazy images. The qualitative and quantitative evaluations demonstrate that our approach yields better results than previous physically-based image dehazing techniques, and favorably compares with the deep learning dehazing approaches.

I. INTRODUCTION

Outdoor images are frequently altered by haze, which is a natural phenomenon resulting from the scattering and absorption of light by tiny particles suspended in the atmosphere. This can selectively attenuate the light spectrum, leading to decreased visibility, lost contrast, color artifacts and additional noise in the images. Consequently, restoring these images is crucial in various outdoor applications relying on visual appearance to (re-)identify objects [1], such as visual surveillance and automated driving assistance.