Norm Constraints Pyramid for Image Dehazing

Dark channel prior-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on the accuracy of the assumptions used in the target scenes, which incurs color distortion and brightness reduction when the models are used for real-world hazy images. We propose a norm constraints pyramid framework to improve the generalization performance of dehazing. First, a local color adaptive correction approach is devised to ascertain whether there is any color bias and thereafter rectify it automatically. Furthermore, multiple norm constraint methods are developed to improve the transmission and accomplish the first image removal. Finally, a non-linear enhancement method is created via this restriction that precisely modifies the brightness of an image. Through extensive experiments, we demonstrate that our framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.