In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At la...

Recent single image de-haze approaches assume the atmospheric light is the only illumination in one haze image and use a globally constant to image de-haze. However, every local pixels in an outdoor image is actually under the influence of non-uniform illumination in real world. The accuracy of the environmental illumination estimation has a great influence on the result, so the traditional haz...

This paper proposes a fast method to enhance the image, which is taken for the bad weather such as fog, haze. Using pixel-based median channel offog image, we can estimate atmospheric light. As a result, high-quality image can be recovered with lower computation complexity compared to patch-based dark channel prior.

Retinal image quality assessment is an essential task for the diagnosis of retinal diseases. Recently, there are emerging deep models to grade images. However, current either directly transfer classification networks originally designed natural images or introduce extra priors via multiple CNN branches independent CNNs. The purpose this work address by a simple model. We propose dark and bright...

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 ...