Recently video surveillance in smart city projects is becoming more and more popular. Video surveillance plays an important role for security purposes and it is required high-quality images for video image analysis and recognition. 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, a new method was proposed for a single image and video dehazing. Many complex methods exist for removing haze from hazy images. In this paper, a method was proposed 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. The proposed video dehazing method is implemented in three steps. They are i) convert the video into frames of single images, ii) remove haze in single images and iii) convert the sequence of output frames into the video format. All these have been implemented using Matlab2020b software. To extract the global atmospheric light accurately, multiple prior DCP and BCP underlying hazy images were exploited. In addition, the effectiveness of the proposed method qualitatively against existing techniques was explored. The computation speed of the proposed method is 62% faster and 64% faster than existing methods for 640 x 480(VGA) resolution images. The experimental result shows that our proposed algorithm enhances the colour fidelity reduces the halo effect and improves the efficiency of video dehazing.
Author
(s) Details
U.Hari
Department of Electronics and Communication Engineering, Saveetha
Engineering College, Chennai, Tamil Nadu, India.
A.Ruhan
bevi
Department of Electronics and Communication Engineering, SRM
Institute of Science and Technology, KTR Campus, Chennai, Tamil Nadu, India.
Please see the book here:- https://doi.org/10.9734/bpi/srnta/v7/2590
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