Image Dehazing Algorithms Using Dark Channel Prior Based On Filtering

As we all know, people can not see clearly in the foggy day. As a result, lots of traffic accidents will be happened due to the fog and it brings inconvenience to people’s daily life. For researchers, owning to the low contrast and blur pictures taken in the foggy day, they can not get the useful information from the haze image. Image dehazing algorithm is to make a haze image becomes more clear. It has been widely used in many areas, such as target recognition, object detection, etc. This paper mainly studies the fast haze removal from single image based on dark channel prior and it will be discussed from the following aspects:1) Pointed out the presence of halo effects and color distortion problem in the existing algorithm based on the dark channel prior. Thereby a fast single image defogging algorithm based on edge-maximum filter has been proposed. Firstly, an edge-maximum filter is used to recover the undervalued dark pixels obtained by edge detection, which is to receive an accurate transmission map and eliminate the halo effects. Then in order to gain a high contrast dehazing image, all the dark pixels are multiplied by a scaling factor to improve the dynamic ranges of the transmission. Finally, two brightness thresholds and one flat threshold are set to eliminate the influence of high light objects in the image and obtain a more accurate airlight, which keeps a high color fidelity in the dehazing image. The simulation results show that the proposed method, compared with other algorithms, could eliminate the halo effects and achieve the dehazing image with high contrast and high color fidelity, especially for the images containing high light objects or rich details. Meanwhile, the computational speed is also improved.2) The dehazing algorithm which used the edge-maximum filter can not acquire the transmission adaptively and the dehazing image always looks dark. Consequently, a self-adaptive transmission estimation and brightness stretching for image dehazing algorithm has been came up with to solve the problem. It combined the known dark channel points with transmission equation to obtained the atmospheric scattering coefficient and then calculated the accurate transmission. Finanly, through the histogram distribution of dehazing image, it decide whether to stretch the dehazing image’s brightness. The simulation results show that the proposed method could eliminate the halo effects and achieve the dehazing image with high contrast and high color fidelity. Meanwhile, it can also dehazing some specific images such as road images and remote sensing images.3) Above methods can not handle the fog image which have depth-discontinuities, especially some small gaps. To address this problem, we using the fast weighted median filter to optimize the transmission. This algorithm makes full use of the advantages of rapid weighted median filter, which maintains the edge and corner, and fast. Experimental results show that, with this method, the dehazing result will be better in the fog image which includes some small gaps, and the speed is further improved.