Shanghai Jiaotong Daxue xuebao (Mar 2023)
An Image Dehazing Method for UAV Aerial Photography of Buildings Combining MCAP and GRTV Regularization
Abstract
Aimed at the problems of low resolution, low contrast,and dark color of images recovered by traditional dehazing processing, an improved images dehazing method is proposed and applied to the unmanned aerial vehicle (UAV) aerial building image processing. First, to solve the problem that the value of global atmospheric light is easily affected by the scene objects, a method of atmospheric light with minimum variance of color attenuation prior projection is proposed. The difference image of brightness and saturation is constructed to solve the region where the minimum variance occurres, and the estimation of global atmospheric light is determined. Then, the regional atmospheric light is fused with the global atmospheric light by using the depth information of the image scene, and a new atmospheric light image is obtained. Finally, the haze line based on the non-local information prior theory in view of the transmittance is optimized. Moreover, this paper proposes a method based on the theory of haze line and guide relative to the total variation regularization algorithm. The transmission rate is fixed through calculating transmittance reliability function. A large amount of useless texture information existing in the image is eliminated, which enhances the precision of transmission rate estimation. It effectively improves the image quality of thick haze and abrupt depth-of-field in UAV aerial shooting scene. The experimental results show that, compared with other algorithms, the average gradient, contrast, haze aware density evaluator, and blur coefficient of the recovered images are improved by 12.2%, 7.0%, 11.9%, and 12.5%, respectively. The operation time of the proposed algorithm is shorter than that of some other algorithms, and the processed aerial images are clearer, which are more consistent with the visual perception of human eyes.
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