| 引用本文: | 黄鹤,胡凯益,郭璐,王会峰,朱礼亚.改进的海雾图像去除方法[J].哈尔滨工业大学学报,2021,53(8):81.DOI:10.11918/202008105 |
| HUANG He,HU Kaiyi,GUO Lu,WANG Huifeng,ZHU Liya.Improved defogging algorithm for sea fog[J].Journal of Harbin Institute of Technology,2021,53(8):81.DOI:10.11918/202008105 |
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| 改进的海雾图像去除方法 |
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黄鹤1,胡凯益1,郭璐2,王会峰1,朱礼亚1
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(1.长安大学 电子与控制工程学院,西安 710064; 2.西北工业大学 无人机系统国家工程研究中心,西安 710072)
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| 摘要: |
| 针对传统去雾处理复原得到的图像清晰度和对比度较低、整体颜色偏暗的问题,提出了一种改进的图像去雾方法,应用于海上含雾图像处理中。首先,获取含雾图像的暗通道及最小值图像,将含雾图像转换到HSV颜色空间计算各个像素点的颜色衰减率,对其按降序排序取前10%中的最小值作为亮暗部分界阈值,据此计算出HSV暗部图像区域（IHSV_dark）。通过引入变差函数来判断像素点是否来自于高亮区域,并获得基于变差函数的变差暗部图像区域（IVAM_dark）。对两个暗部图像区域做并运算,得到用于估计暗区域大气环境光值的暗部图像Idark。将像素值进行递减排序,选取前1‰的像素点所对应雾化降质图像像素点集合的平均值作为Adark的值。其次,提出一种基于多级权重相对总变差模型的去纹理方法,对最小值图进行滤波作为粗估计的透射率图,并使用透射率函数对其进行调整,弱化亮部图像的去雾,增强暗部图像的去雾。最后,提出一种最小方差中值引导滤波算法对调整后的透射率进行优化,根据雾天图像降质模型得到复原后的清晰图像。实验结果表明,提出的算法与基于暗通道先验理论以及融合变差函数和形态学滤波的去雾算法相比,获得的复原图像信息熵、平均梯度、对比度及雾霾浓度评价指标（FADE）等指标均有显著提升,更加清晰。 |
| 关键词: 最小方差中值引导滤波 颜色衰减先验 图像处理 变差函数 去雾 |
| DOI：10.11918/202008105 |
| 分类号:TP391.4 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFB1600600);装备预研领域基金(61403120105);陕西省自然科学基础研究计划面上项目(2019JM-611);陕西省创新人才推进计划-青年科技新星项目(2019KJXX-028);长安大学中央高校基本科研业务费专项(1,1) |
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| Improved defogging algorithm for sea fog |
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HUANG He1,HU Kaiyi1,GUO Lu2,WANG Huifeng1,ZHU Liya1
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(1.School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China; 2.UAV National Engineering Research Center, Northwestern Polytechnical University, Xi’an 710072, China)
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| Abstract: |
| In view of the problems of poor clarity, low contrast, and dark color of traditional image defogging algorithms, an improved defogging algorithm was proposed for processing images with sea fog. First, the dark channel image and the minimum image of foggy image were obtained. The foggy image was converted into the HSV color space to calculate the color decay rates of each pixel, which were then sorted in descending order, and the minimum value of the first 10% values was taken as the threshold of the bright and dark parts. On this basis, the dark part (IHSV_dark) of the foggy image was calculated. The variational function was introduced to determine whether the pixel in the image was from the bright area, and the variational dark part (IVAM_dark) based on the variational function was obtained. Then, the two dark parts were combined to calculate the dark part image (Idark), which was used to estimate the ambient light value of the dark area. The pixel values were sorted in descending order, and the average value of the degraded image pixels corresponding to the top 1‰ pixel values was selected as the value of Adark. Next, a method for removing textures was proposed based on the multi-level weight relative total variation model. The minimum image was filtered as the rough estimate of the transmittance image, which was adjusted by the transmittance function to weaken the defogging of the bright image and enhance the defogging of the dark image. Finally, a minimum variance median guide filter algorithm was proposed to optimize the adjusted transmittance, and the clear image was acquired based on the foggy image degradation model. Experimental results show that the information entropy, average gradient, contrast, and fog aware density evaluator (FADE) of the restored image obtained by the proposed algorithm were significantly improved compared with traditional algorithms. |
| Key words: minimum variance median guide filter color attenuation prior image processing variational function defogging |
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