采用自适应全局亮度补偿的夜间去雾算法

陈志恒; 严利民; 张竞阳

采用自适应全局亮度补偿的夜间去雾算法

上海大学微电子研究与开发中心，上海 200444

基金项目:

国家自然科学基金 61674100

详细信息

作者简介:
陈志恒（1994-），男，硕士研究生，研究领域为视频清洗技术

通讯作者:
严利民（1971-），男，博士，副教授，研究领域为集成电路设计及系统集成、新型显示技术和计算机视觉。E-mail：yanlm@shu.edu.cn

中图分类号: TP391
计量
- 文章访问数: 210
- HTML全文浏览量: 41
- PDF下载量: 42
- 被引次数: 0
出版历程
- 收稿日期: 2020-04-26
- 修回日期: 2020-05-21
- 刊出日期: 2021-10-20

Nighttime Dehazing Algorithm with Adaptive Global Brightness Compensation

Microelectronics R & D Center, Shanghai University, Shanghai 200444, China

摘要

摘要: 针对夜间雾霾天气情况下还原的去雾图像存在颜色失真、纹理损失严重、去雾效果差等问题，本文提出了一种夜间去雾算法，采用自适应全局亮度补偿、同态滤波、限制对比度自适应直方图均衡化算法以及联合双边滤波对降质图像进行处理，结合大气散射模型得到还原的去雾图像。实验结果表明，该算法的夜间去雾效果好、处理速度快，较对比算法在对比度、平均梯度以及信息熵上均有改善，有效减少了还原图像的颜色失真、纹理损失。
- 夜间去雾 /
- 自适应全局亮度补偿 /
- 同态滤波 /
- 限制对比度自适应直方图均衡化算法 /
- 联合双边滤波
Abstract: To address the problems of color distortion, heavy texture loss, and poor dehazing effect in the dehazing images of hazing nights, this study proposes a night-time dehazing algorithm. Adaptive global brightness compensation, homomorphic filtering, contrast limited adaptive histogram equalization algorithm, and joint bilateral filtering were used to process the hazing images, and the dehazing images were obtained by combining the atmospheric scattering model. The experimental results show that this method has a better night dehazing effect and faster processing speed than the comparison algorithms. The contrast, mean gradients, and entropy are improved, and the color distortion and texture loss are effectively reduced.
- nighttime dehazing /
- adaptive global brightness compensation /
- homomorphic filtering /
- contrast limited adaptive histogram equalization algorithm /
- joint bilateral filtering

HTML全文

图 1 本文算法实现过程效果

Figure 1. The effect of the algorithm in this paper

下载: 全尺寸图片幻灯片

图 2 自适应全局亮度补偿流程图

Figure 2. Flow chart of adaptive global brightness compensation

下载: 全尺寸图片幻灯片

图 3 透射率获取流程图

Figure 3. Flow chart of transmittance acquisition

下载: 全尺寸图片幻灯片

图 4 视觉效果对比

Figure 4. Visual effect comparison

下载: 全尺寸图片幻灯片

表 1 几种客观评价结果的比较

Table 1. Comparison of several objective evaluation results

Image	Method	c	g	e	t/s
(1) in Fig.4(600×431)	Original	43.68	3.36	7.52	-
	Ref. 14	60.81	2.33	5.46	2.93
	Ref. 8	46.19	2.81	6.88	3.78
	Ref. 15	62.23	4.16	7.13	12.73
	Ours	91.41	4.83	7.38	1.67
(2) in Fig.4(600×431)	Original	72.19	4.25	7.53	-
	Ref. 14	74.27	2.72	5.34	2.62
	Ref. 8	69.14	3.51	6.88	3.66
	Ref. 15	87.15	4.86	7.36	12.81
	Ours	93.31	5.81	7.44	1.39
(3) in Fig.4(600×431)	Original	54.76	4.42	7.35	-
	Ref. 14	50.54	2.69	5.99	2.37
	Ref. 8	49.19	3.57	6.77	4.01
	Ref. 15	83.15	5.36	7.24	12.88
	Ours	95.35	6.78	7.43	1.54
(4) in Fig.4 (600×431)	Original	88.22	4.44	7.62	-
	Ref. 14	86.25	4.61	5.78	2.66
	Ref. 8	76.13	4.71	6.98	3.73
	Ref. 15	121.71	5.77	7.23	12.91
	Ours	137.34	6.43	7.57	1.44

下载: 导出CSV

参考文献(15)

[1]	ZHANG Jing, CAO Yang, FANG Shuai, et al. Fast haze removal for nighttime image using maximum reflectance prior[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7418-7426.
[2]	董浩伟, 陈洁. 一种基于大气散射模型和Retinex的红外图像去雾算法[J]. 红外技术, 2019, 41(4): 347-356. http://hwjs.nvir.cn/article/id/hwjs201904009 DONG Haowei, CHEN Jie. Infrared image defogging algorithm based on atmospheric scattering model and Retinex[J]. Infrared Technology, 2019, 41(4): 347-356. http://hwjs.nvir.cn/article/id/hwjs201904009
[3]	YU Teng, SONG Kang, MIAO Pu, et al. Nighttime single image dehazing via pixel-wise alpha blending[J]. IEEE Access, 2019, 7: 114619-114630. doi: 10.1109/ACCESS.2019.2936049
[4]	陆士猛, 刘昌锦. 无人机侦察图像快速去雾算法[J]. 红外技术, 2015, 37(10): 847-851. http://hwjs.nvir.cn/article/id/hwjs201510008 LU Shimeng, LIU Changjin. The fast haze removal algorithm for UAV reconnaissance image[J]. Infrared Technology, 2015, 37(10): 847-851. http://hwjs.nvir.cn/article/id/hwjs201510008
[5]	黄黎红. 一种基于单尺度Retinex的雾天降质图像增强新算法[J]. 应用光学, 2010, 31(5): 728-733. https://www.cnki.com.cn/Article/CJFDTOTAL-YYGX201005013.htm HUANG Lihong. Fog-degraded image enhancement based on singlescale Retinex[J]. Journal of Applied Optics, 2010, 31(5): 728-733. https://www.cnki.com.cn/Article/CJFDTOTAL-YYGX201005013.htm
[6]	HE K, SUN J, TANG X. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(12): 2341-2353. http://www.robots.ox.ac.uk/~vgg/rg/papers/hazeremoval.pdf
[7]	HE K, SUN J, TANG X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(6): 1397-1409.
[8]	徐兴贵, 杨平, 刘永利. 基于全尺度Retinex算法的夜间图像去雾[J]. 微电子学与计算机, 2017, 34(7): 132-136 https://www.cnki.com.cn/Article/CJFDTOTAL-WXYJ201707028.htm XU Xinggui, YANG Ping, LIU Yongli. Night image dehazing based on full-scale Retinex algorithm[J]. Microelectronics & Computer, 2017, 34(7): 132-136. https://www.cnki.com.cn/Article/CJFDTOTAL-WXYJ201707028.htm
[9]	丁雪梅, 李为相, 毛祥宇. 基于Retinex和对比度约束的夜间去雾[J]. 计算机工程与设计, 2018, 39(9): 2856-2860. https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201809028.htm DING Xuemei, LI Weixiang, MAO Xiangyu. Night dehazing based on Retinex and contrast constraint[J]. Computer Engineering and Design, 2018, 39(9): 2856-2860. https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201809028.htm
[10]	Srinivasa G Narasimhan, Shree K Nayar. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48(3): 233-254. doi: 10.1023/A:1016328200723
[11]	李炼, 李维嘉, 吴耀中. 基于红色暗通道先验理论与CLAHE算法的水下图像增强算法[J]. 中国舰船研究, 2019, 14(S1): 175-182. https://www.cnki.com.cn/Article/CJFDTOTAL-JCZG2019S1027.htm LI Lian, LI Weijia, WU Yaozhong. An underwater image enhancement algorithm based on RDCP and CLAHE[J]. Chinese Journal of Ship Research, 2002, 48(3): 233-254. https://www.cnki.com.cn/Article/CJFDTOTAL-JCZG2019S1027.htm
[12]	成宝芝, 张丽丽. 基于双边滤波的最优波段子空间高光谱异常目标检测[J]. 液晶与显示, 2019, 34(9): 897-904. https://www.cnki.com.cn/Article/CJFDTOTAL-YJYS201909009.htm CHENG Baozhi, ZHANG Lili. Optimal bands subspace anomaly detection for hyperspectral imagery based on bilateral filtering[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(9): 897-904. https://www.cnki.com.cn/Article/CJFDTOTAL-YJYS201909009.htm
[13]	杨燕, 李一菲, 岳辉. 一种自适应的多级透射率估计去雾算法[J]. 光子学报, 2019, 48(3): 310001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201903023.htm YANG Yan, LI Yifei, YUE Hui. An adaptive multiscale transmission estimation dehazing algorithm[J]. Acta Photonica Sinica, 2019, 48(3): 310001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201903023.htm
[14]	高强, 胡辽林, 陈鑫. 基于暗通道补偿与大气光值改进的图像去雾方法[J]. 激光与光电子学进展, 2020, 57(6): 150-156. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201507018.htm GAO Qiang, HU Liaolin, CHEN Xin. Image dehazing method based on dark channel compensation and improvement of atmospheric light value[J]. Laser & Optoelectronics Process, 2020, 57(6): 150-156. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201507018.htm
[15]	LI Y, TAN R T, Brown M S. Nighttime haze removal with glow and multiple light colors[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 226-234.