Single Image Dehazing Algorithm Based on Sky Segmentation Bad weather reduces the imaging quality of the intelligent vision system, such as haze and fog. Thus, haze removal has received wide attention from researchers. Most algorithms often suffer from color distortion and edge loss when dealing with the images containing large areas of sky. In this paper, we propose an effective dehazing method. The iterative threshold segmentation is used to segment the sky region out from the image, and then the brightness of the sky region is adjusted to increase clarity. The improved dark channel priori is used to process the rest regions. The transmission map is estimated by fast bilateral filtering. Finally, the two regions are merged together to get the haze removal result. Our algorithm achieves a clear and natural hazefree image, and has a faster processing speed. Meanwhile, it is universal and realtime in practical application.

A convex single image dehazing model via sparse dark channel prior Abstract   In this paper, we present a convex model for single image dehazing via a sparse dark channel prior. Our work is based on an observation that the number of bright pixels is very small in the dark channel of a hazefree image, but significantly increases in that of a hazy image due to the existence of bright atmosphere light. Since the dehazing problem is inherently ambiguous, we first reformulate the degradation model of hazy images into an equivalent form where the transmission and the image variable are decoupled. With the above formulation and observation, we propose the convex model to recover the hazefree image whose dark channel is assumed to be sparse. The proposed objective function consists of four terms: a datafitting term, an l1 regularization term for the dark channel of the hazefree image, and two total variation regularization terms for both the hazefree image and the transmission map. We develop an efficient alternating direction method of multipliers (ADMM) to tackle the proposed convex model. Extensive experiments on real hazy images illustrate that our method outperforms the stateoftheart methods.