The analysis of polarized filtered images has been proven useful in image dehazing. However, the current polarization-based dehazing algorithms are based on the assumption that the polarization is only associated with the airlight. This assumption does not hold up well in practice since both object radiance and airlight contribute to the polarization. In this study, a new polarization hazy imaging model is presented, which considers the joint polarization effects of the airlight and the object radiance in the imaging process. In addition, an effective method to synthesize the optimal polarized-difference (PD) image is introduced. Then, a decorrelation-based scheme is proposed to estimate the degree of polarization for the object from the polarized image input. After that, the haze-free image can be recovered based on the new polarization hazy imaging model. The qualitative and quantitative experimental results verify the effectiveness of this new dehazing scheme. As a by-product, this scheme also provides additional polarization properties of the objects in the image, which can be used in extended applications, such as scene segmentation and object recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.