For traditional dark channel prior (DCP) imaging through haze environments, intensity information acts as the carrier to acquire reflective character of dehazed target image. We introduce polarization auxiliary into DCP process for achieving better performance that can improve detection or tracking. In this paper, a system with split-amplitude structure is designed enable real-time acquisition ...

Image dehazing, as a common solution to weather-related degradation, holds great promise for photography, computer vision, and remote sensing applications. Diverse approaches have been proposed throughout decades of development, deep-learning-based methods are currently predominant. Despite excellent performance, such computationally intensive these recent advances amount overkill, because imag...

Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion. In this paper, following approaches based on Dark channel prior, we propose a simple yet effective approach using Retinex theory Taylor series expansion for dehazing, referred as ‘RDT’. Existing methods do not handle shift glow very ...

Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In this paper, we propose a new transmission estimated method to improve the visibility of single input image (with fog or haze),...

Remote sensing image dehazing is challenging because it is massively ill-posed and the haze is dependent on the unknown depth information. Haze removal based on dark channel prior is effective, and refining the transmission map with Gaussian filter will produce a god result. But need to improve the naturalness and sharpness and effectiveness of images and to remove fine haze . So Proposes new a...