Paper
15 November 2017 Image defog algorithm based on open close filter and gradient domain recursive bilateral filter
Daqian Liu, Wanjun Liu, Qingguo Zhao, Bowen Fei
Author Affiliations +
Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106053T (2017) https://doi.org/10.1117/12.2295866
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what’s more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daqian Liu, Wanjun Liu, Qingguo Zhao, and Bowen Fei "Image defog algorithm based on open close filter and gradient domain recursive bilateral filter", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053T (15 November 2017); https://doi.org/10.1117/12.2295866
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