A Unified Variational Model for Single Image Dehazing

Haze is a common weather phenomenon, which hinders many outdoor computer vision applications such as outdoor surveillance, navigation control, vehicle driving, and so on. In this paper, a simple but effective unified variational model for single image dehazing is presented based on the total variation regularization. From the perspective of the relationship between image dehazing and Retinex, the dehazing problem can be formulated as the minimization of a variational Retinex model. The proposed variational model incorporates two
${\ell _{1}}$
-norm regularization terms to constrain the scene transmission and the inverted scene radiance respectively, which can be better applied into image dehazing field. Different from the conventional two-step framework, our proposed model can simultaneously obtain the accurate transmission map and the recovered scene radiance by integrating the transmission estimation stage and the image recovery stage into the unified variational model. The entire optimization of the proposed unified variational model can be solved by an alternating direction minimization scheme. The experiments on various simulated and real-world hazy images indicate that the proposed algorithm can yield considerably promising results comparative to several state-of-the-art dehazing and enhancement techniques.