Single Image Haze Removal Algorithm Research

Haze, which is caused by scattering of sunlight by water vapor and other scattering sources in atmosphere, can significantly reduce the contrast of images. Removing these image hazes are very important in traffic navigation, urban and rural road transport, target recognition, intelligent transportation systems, surveillance systems, remote sensing systems and military and defense fields. As a result, intensive research interests in computer vision and digital image processing have been centered on this subject.In 2009 He Kaiming et al. proposed an algorithm for the single image haze removal using DCP (Dark Channel Prior) on CVPR (Computer Vision and Pattern Recognition), which is one of the most important conferences in the field of computer vision. Based on the atmospheric image-forming principle and atmospheric attenuation model, the algorithm proposed a new prior knowledge. Then all unknown parameters are estimated from the prior knowledge. By this algorithm, very good images are recovered. But it costs a lot of time, hence the algorithm can not reach the requirements of practical application. In this paper, aiming to the slowness of DCP, several improvements have been conducted in the following aspects:(1) By studying the haze removal process of single image using DCP algorithm, it is found that there is block effect in the original haze transmission, leading to halo in the recovery results. And the optimization process using soft matting method in DCP takes much time. In order to increase the speed of optimization, we have improved the optimization algorithm.Considering the halo caused by block effect at the edge of recovered image, we filter the haze transmission to smooth the haze transmission to eliminate the impact of the block effect on the recovery results. Since the block effect has a greater impact on the edge of the image than on the flat areas, in order to improve the filtering speed, only the image edge is filtered. According to image structural information and detection method, a special filter is designed, which uses different structures for various directions of the image edge. It is known that pixels in the same structure neighborhood are related in space, which means that they have similar gray values. The gray values of pixels at the edge are estimated by neighbor information on neighborhood pixel statistics. By this special filter, our algorithm is about 10 times faster than DCP and good recovery images are obtained.(2) A faster single image haze removal algorithm is designed based on the DCP prior knowledge and atmospheric scattering light. First the global atmospheric light is calculated using the DCP prior knowledge. Second, the airlight is estimated. Third, the global atmospheric light and airlight are used to solve the haze transmission. This transmission does not need to be optimized and the recovered images can be directly obtained using the atmospheric attenuation model. Our algorithm combines both the priorities of the accurate estimation of global atmospheric light by DCP and the fast calculation of airlight, which makes it 14 times faster than Kaiming He's algorithm. The recovered images are similar to and even better than that of DCP.