基于超像素分割和双通道融合的弹载图像去雾方法

MISSILEBORNE IMAGE DEFOGGING METHOD BASED ON SUPER-PIXEL SEGMENTATION AND DUAL CHANNEL FUSION

  • 摘要: 针对暗通道先验去雾算法在弹载图像中适应性差的问题,提出基于超像素分割和双通道融合的弹载图像去雾方法。对雾图进行预处理;进行超像素分割,获取改进亮暗通道;采用双通道融合算法估计出透射率;提出快速加权引导滤波算法细化透射率;并根据弹载图像特点获取大气光值,求得去雾图像;调节其亮度和饱和度。仿真结果显示,该算法能准确估计弹载有雾图像的大气光值和透射率,有效避免对明亮区域去雾后颜色失真的问题,同时去雾图像细节清晰,场景对比度恢复好,可视性强,在综合评价上优于对比算法。

     

    Abstract: Aimed at the problem that the dark channel prior dehazing algorithm has poor adaptability in the missile image, a missile image dehazing method based on super-pixel segmentation and dual-channel fusion is proposed. The haze image was preprocessed. The super-pixel segmentation was performed to obtain improved bright and dark channels. The transmittance was estimated by a dual-channel fusion algorithm. A fast weighted guided filtering algorithm was proposed to refine the transmittance. The light value was obtained to obtain the dehazed image. Its brightness and saturation were adjusted. The simulation results show that the proposed algorithm can accurately estimate the atmospheric light value and transmittance of the fog-loaded image, which effectively avoids the problem of color distortion after defogging the bright area. The dehazing images have clear details, good scene contrast restoration, and strong visibility, which is superior to the contrast algorithm in comprehensive evaluation.

     

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