Image Enhancement System Based on Improved Dark Channel Prior

Abstract

Abstract. In order to reduce the bad influence of hazes on the computer vision system, an image dehazing algorithm based on dark channel prior is proposed. Firstly, we analyze the dark channel prior based image dehazing algorithm. Since the complexity of the algorithm is great and the acquired images have halo artifacts effect, the neighborhood similarity method, which gets the difference of the value of dark channel between every pixel and its nearest eight pixels, is introduced. The pixel of minimal difference is redefined as new dark channel. Besides, the adaptive gray stretch and local contrast enhancement algorithms are proposed to increase brightness and definition of the video. Finally, realization of the improved image enhancement algorithms based on DM6467 is analyzed in detail. The results indicate that the method can effectively improve the image quality. IntroductionComputer vision system has been widely used in all aspects of social production and daily life, such as urban transportation, security facilities, video monitoring, etc. However, in foggy weather, the scene light is absorbed or scattered by the atmosphere with a large number of suspended particles (dust particles and water droplets, etc.).Camera image quality deteriorates with poor visibility and color distortion, which greatly affects the reliability and robustness of computer vision system.Image dehazing is a hotspot in the fields of computer vision and image processing. He et al.[1] proposed a simple but effective image prior-dark channel prior to remove haze from a single input image, which can achieve great haze removal effect. The method has been widely in image processing field [2,3].However, as it is based on the assumption that the transmission is locally constant, the patch size will affect the quality of dehazed images [4]. A large patch size leads to bright atmosphere but serious halo artifacts, while a small one can achieve nice dehazing results with little halo artifacts but dim atmosphere.And the process of optimizing the medium transmission in the improved algorithm [5,6] costs too much time, while the computational complexity is too high to be real-time operating for high resolution image.In this paper, an improved dark channel prior based image dehazing algorithm is designed by introducing neighborhood similarity method to reduce halo artifacts without soft matting. In order to improve image quality after image dehazing, some image enhancement algorithms are introduced, such as linear gray expanding and local contrast enhancement. Then, an image enhancement system is developed by applying the image processing algorithms based on the TI's DaVinci technology. The system meets the real-time and good interaction demands.The rest of the paper is organized as follows. Section 2describes the video image enhancement architecture. In Section 3, the proposed method for image enhancement is detailed. Experimental results on image enhancement performance are illustrated in Section 4 followed by the conclusions in Section 5.