Image dehazing based on polarization information and deep prior learning

(objective)Generally, images taken under hazy weather have lower visibility and contrast. Therefore, how to improve their visibility and contrast and recover the image information is critical. Various dehazing algorithms have been proposed based on light intensity information. However, these methods mostly lack sufficient scattering information about atmospheric particles. Polarization supplies extra information independent of intensity, which can be used to effectively remove scattered light from haze particles, making it possible to help image dehazing. (methods)Therefore, this paper proposes a new image dehazing algorithm based on polarization information and deep prior learning (polarization-based unsupervised dehazing network, PUDN). Polarization information can provide more information about the image, while prior knowledge can constrain and optimize the network. This algorithm can remove haze without pretraining or corresponding ground-truth image. (result)Experimental results show that introducing polarization information and prior knowledge can improve the visibility and contrast of haze images, significantly improve haze image quality. Among them, the visible edge of heavy-haze image is improved by 71% on average, and the contrast is improved by 31 %. The dehazing performance is superior to that of traditional and supervised learning methods. Our method provides a new direction for image dehazing processing.