摘要
针对高分辨率遥感卫星图像采用单一算法难以有效去除不均匀云雾的问题,提出一种基于图像分割和暗原色先验改进方法相结合的优化算法。采用图像分割技术将原云雾图像分割成浓雾部分和淡雾部分。浓雾部分采用加权多尺度Retinex算法进行局部增强去雾处理;淡雾部分采用改进暗原色方法,将暗原色图像去雾模型由RGB颜色空间转换到HSI颜色空间,提取亮度分量,获取准确的大气光值,并采用容差机制优化获取大气透射率,在此基础上采用自动色阶法增强处理,获取去除云雾后的图像。实验对比表明提出的算法能够很好地还原图像细节,有效恢复图像的颜色和清晰度。
Aiming at the problem that nonuniform cloud is difficult to remove effectively by using single algorithms for high resolution remote sensing satellite images,an optimization algorithm based on image segmentation and improved dark channel prior method is proposed.The original cloud image is segmented into a dense fog area and a thinner fog area by the image segmentation technique.The dense fog area adopts weighted multiscale Retinex algorithm to realize local enhancement and remove the fog.The thinner fog area adopts the improved dark color method,transforming the dark color image defogging model from RGB color space to HSI color space,extracting the luminance component,and obtaining accurate atmospheric optical values.The atmospheric transmittance is optimized by the tolerance mechanism,and the defogged image is obtained by enhancement of the automatic gradation method.Experimental results show that the proposed algorithm can restore image details and recover image color and clarity effectively.
作者
龚文斌
石章松
韦华
Gong Wenbin;Shi Zhangsong;Wei Hua(Colloge of Weaponry Engineering,Naval University of Engineeing,Wuhan,Hubei 430000,China;Naval Staff of PLA,Beijing 100841,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第6期286-293,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(41771487,41571441)。
关键词
机器视觉
去雾处理
高分辨率遥感图像
暗原色先验法
图像分割
machine vision
defogging
high resolution remote sensing image
dark-channel prior method
image segmentation
