An automatic white balance method via dark channel prior

Overview: Color is an important feature in the field of computer vision, as it relates to the practical vision problems such as designing an object recognition or classification tasks. For human beings, the brain will let people feel who is in a natural white light environment, while this is difficult for a computer, Given a pixel with blue color, how can a computer distinguish that the color is resulted by a white object under a blue lighting source or a blue object under a white lighting source? In order to address this question, we need to remove illuminated color of the light source, which is specified as white balance problem. In general, there are three types of color constancy approaches, summarized as statistics-based, traditional learning based and deep learning based ones, respectively. Although the latter two have achieved excellent results, but due to the low speed, it is not practical. The statistics-based method has the problem of white region or white points detection error which leads to white balance failure. In order to solve this problem this paper proposes a white balance method based on dark channel prior. First, get the dark channel image, extract the white region in the image according to the dark channel, and then remove the region with high saturation. Finally, in order to correct the color and ensure that the image brightness does not change, we calculate the correction gain in the CIE-XYZ color space relative to the luminance channel Y. By removing the region with high saturation, we define a threshold transformation. Through a large number of experiments, we obtain a threshold K which can effectively eliminate the high light area, and this makes the white area more stable. In order to make the algorithm run on a low frequency ARM, we tested the white balance effect under different down sampling which shows that the speed is only 5ms under the size of 1/16, and the effect does not have much impact. Experimental results show that our algorithm has achieved good results both in subjective and objective evaluation compared with some classical algorithms. Meanwhile, we compared our method with a Nikon camera. Our method is excellent, and it's better than Canon in detail. We use our algorithm instead of the white balance algorithm in HI3516D. The test shows that our method can achieve 150 frame/sec and the effect is better than the algorithm in HI3516D.