基于Pyramid-Kuwahara滤波器的航拍图像去雾方法
Aerial images defogging method based on Pyramid-Kuwahara filter
马天 1黄鹤 1李战一 1杨澜 2高涛 2王会峰3
作者信息
- 1. 长安大学电子与控制工程学院,西安,710064;西安市智慧高速公路信息融合与控制重点实验室,西安,710064
- 2. 长安大学信息工程学院,西安,710064
- 3. 长安大学电子与控制工程学院,西安,710064
- 折叠
摘要
针对目前无人机航拍图像去雾方法存在难以兼顾不同景深区域去雾、边缘细节丢失过多、效果不明显等问题,提出一种基于Pyramid-Kuwahara滤波器的去雾方法.首先,根据改进的暗通道先验方法求解大气光估计和透射率;其次,设计了一种Pyramid-Kuwahara滤波器,利用多尺度的滤波器对大气光进行优化和细节提取;然后,结合设计的滤波器,提出一种多层滤波相对总变分模型MFRTV,以增强透射率中的细节信息;最后,通过优化大气散射模型来计算透射率和大气光图,并获得去雾后的复原图像.实验结果证明,提出的去雾算法在不同的景深区域内均能有效地恢复图像的细节,使实验图像中的雾气明显得到消除.同时,该算法还能在更远景深的范围内去除雾气,以提供更好的主观视觉效果,并提高图像的信息丰富度.和同类算法相比,提出的算法的复原图像的信息熵、FADE、结构相似度和平均梯度等参数指标均有显著提升.
Abstract
Aiming to address the challenges of the current UAV aerial image de-fogging method,including difficulties in considering de-fogging for different depths of field,excessive loss of edge details,and ineffective results,this paper proposes a de-fogging method based on the Pyramid-Kuwahara filter.Firstly,atmospheric light estimation and transmittance are solved using an improved dark channel prior method.Secondly,a multi-scale filter called Pyramid-Kuwahara is designed to optimize and extract atmospheric light details.Then,a method named MFRTV is proposed to enhance detail information in transmission based on the designed filter.Finally,the restored fog-free image is obtained by utilizing the atmospheric scattering model along with optimized transmittance and atmospheric light maps generated by the algorithm.Experimental results demonstrate that our proposed fog removal algorithm effectively restores image details in different depths of field while significantly reducing fog presence in experimental images.Moreover,it successfully removes fog even at further depths of field achieving in enhanced subjective visual effects and increased information richness compared to other control algorithms.The proposed algorithm exhibits significant improvements in parameters such as entropy,FADE(fog area density estimation),structural similarity index(SSIM),and average gradient.
关键词
图像去雾/滤波器/暗通道/RTV模型Key words
image dehazed/filter/dark channel/RTV引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据