Abstract Image dehazing is an increasingly widespread approach to address the degradation of images natural environment by low-visibility weather, dust and other phenomena. Advances in autonomous systems platforms have increased need for low-complexity, high-performing techniques. However, while recent learning-based image approaches significantly performance, this has often been at expense com...

Image dehazing is of great significance in computer vision and other fields. The performance mainly relies on the precise computation transmission map. However, existing map still does not work well sky area easily influenced by noise. Hence, dark channel prior (DCP) luminance model are used to estimate coarse this work, which can deal with problem estimation area. Then a novel weighted variati...

With the development of convolutional neural networks, hundreds deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss physical model, datasets, network modules, loss functions, evaluation metrics that are commonly used. Then, main contributions various alg...

This paper proposes an improved dark channel prior for removing haze from images. Dark channel prior is an effective method for removing haze. Dark channel is an image in the same size as the hazy image which is obtained by dividing the RGB images into windows and for each window, the minimum of each R, G and B channels are calculated. Then again the minimum of these three values is calculated ...