A novel technique for spatiotemporal dahazing of video image

Abstract

Recently video surveillance in smart city projects is becoming more and more popular. Generally, high quality image is required in video image analysis and recognition. Often bad weather conditions like atmospheric haze, fog, and smoke affect captured outdoor images and result in loss of visibility and poor contrast. In this paper, we propose a new method for a single image and video dehazing. Many complex methods are existing for removing haze from hazy images. In this paper, we propose a method that combines dark channel prior(DCP) and bright channel prior(BCP) along with a guided filtering technique to perform effectively and efficiently by spatiotemporal means in video dehazing. To extract the global atmospheric light accurately, we exploit multiple prior DCP and BCP underlying hazy images. In addition, the rough transmission map is estimated and improvised using a guided filter to get refined transmission. The experimental result shows that our proposed algorithm enhances the colour fidelity reduces the halo effect and improves the efficiency of video dehazing.