智能行车记录仪图像去雾系统的FPGA设计

doi:10.16183/j.cnki.jsjtu.2023.026

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (4): 565-578.doi: 10.16183/j.cnki.jsjtu.2023.026

智能行车记录仪图像去雾系统的FPGA设计

黄鹤^a^,^b, 胡凯益^a^,^b, 杨澜^c(), 王浩^a^,^b, 高涛^c, 王会峰^b

a.长安大学智慧高速公路信息融合与控制西安市重点实验室,西安 710064
b.长安大学电子与控制工程学院,西安 710064
c.长安大学信息工程学院,西安 710064

收稿日期:2023-01-19 修回日期:2023-04-23 接受日期:2023-04-28 出版日期:2024-04-28 发布日期:2024-04-30
通讯作者: 杨澜,正高级工程师,博士生导师,电话(Tel.):029-82334579;E-mail: lanyang@chd.edu.cn.
作者简介:黄鹤(1979-),教授,博士生导师,从事图像处理与信息融合研究.
基金资助:
国家重点研发计划项目(2021YFB2501200);国家自然科学基金面上项目(52172379);国家自然科学基金面上项目(52172324);陕西省重点研发计划项目(2021SF-483);西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金项目(300102323502);中央高校基本科研业务费资助项目(300102323501)

FPGA Design of Image Defogging System in Intelligent Tachograph

HUANG He^a^,^b, HU Kaiyi^a^,^b, YANG Lan^c(), WANG Hao^a^,^b, GAO Tao^c, WANG Huifeng^b

a. Xi’an Key Laboratory of Intelligent Expressway Information Fusion and Control, Chang’an University, Xi’an 710064, China
b. School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
c. School of Information Engineering, Chang’an University, Xi’an 710064, China

Received:2023-01-19 Revised:2023-04-23 Accepted:2023-04-28 Online:2024-04-28 Published:2024-04-30

摘要/Abstract

摘要：

雾霾天气下,交通道路能见度低,导致所采集到的视频画面退化、图像信息模糊,同时考虑传统系统处理实时性不高等问题,基于ZYNQ平台设计了一种图像去雾系统,并应用于智能行车记录仪系统中.首先,针对传统暗通道去雾算法在天空区域存在失真等问题,提出了一种分割天空区域的策略来修正图像复原参数;然后,针对计算全局大气光值时,需对整幅图像的像素排序消耗大量资源的问题,利用现场可编程门阵列(FPGA)并行运算的优势,提出一种帧迭代方法优化求取大气光值,同时优化了引导滤波的硬件设计;最后,将双路高清多媒体接口(HDMI)资源中,一路作为视频输入,另一路作为视频处理输出,搭建实时交通图像视频处理试验平台.试验结果表明,系统针对雾霾天气下的交通视频具有较好的去雾效果,尤其是可以解决天空区域去雾的失真问题.在对分辨率为1 280 像素×720 像素的交通视频去雾时,可以达到30 帧/s的处理速度,满足实时性要求.

关键词: 交通视频, 图像去雾, ZYNQ平台, 实时处理

Abstract:

In hazy weather, due to the low visibility of traffic roads, the video images collected are degraded and the image information is fuzzy. Considering the problem of low real-time processing of the traditional system, an image defogging system based on the ZYNQ platform is designed and applied to the intelligent driving recorder system. First, aiming at the problems of the traditional dark channel defogging algorithm in the sky region, a sky region segmentation strategy is proposed to correct the image restoration parameters. Then, in order to solve the problem that the pixel ordering of the whole image consumes a lot of resources when calculating the global atmospheric light value, a frame iteration method is proposed to optimize the atmospheric light value by making the advantage of the parallel operation on the FPGA platform, and at the same time, the optimization of guided filtering is realized. Finally, using dual-channel high definition multimedia interface (HDMI) resources, a real-time traffic image video processing experimental platform is established by using one channel of the dual HDMI resources as video input and the other as video processing output, and experimental simulation of the algorithm in this paper is conducted. The experimental results show that the system has a good defogging performance for the traffic video in hazy weather, especially in solving the problem of defogging distortion in the sky area. When the traffic video with a resolution of 1 280×720 is defogged, the processing speed can reach 30 frame/s meeting the real-time requirements.

Key words: traffic video, image defogging, ZYNQ platform, real-time processing

中图分类号:

TP391.41

黄鹤, 胡凯益, 杨澜, 王浩, 高涛, 王会峰. 智能行车记录仪图像去雾系统的FPGA设计[J]. 上海交通大学学报, 2024, 58(4): 565-578.

HUANG He, HU Kaiyi, YANG Lan, WANG Hao, GAO Tao, WANG Huifeng. FPGA Design of Image Defogging System in Intelligent Tachograph[J]. Journal of Shanghai Jiao Tong University, 2024, 58(4): 565-578.

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参考文献 18

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子模块名称	功能说明	输入输出说明
天空区域检测模块	天空区域分割	输入帧缓存图像输出分割后的二值数据
暗通道模块	暗通道图像求取	输入RGB图像输出为暗通道像素数据
大气光值求取模块	大气光值计算	天空分割模块中间均值变量阈值倍数
透射率求取模块	图像透射率计算	输入暗通道图像及其全局大气光值,输出粗估计透射率
透射率优化模块	透射率修正	输入灰度图像及粗估计透射率,输出细化透射率
图像复原模块	去雾图像复原	输入RGB图像、细化后的透射率、大气光值,输出去雾图像数据

实验组	去雾算法	H	g_A	r_P	E_FAD	对比度
E1	原始图像	12.600 0	2.844 9	—	1.992 0	53.387 8
	He算法^[7]	14.663 9	4.685 0	24.213 1	0.884 3	62.995 2
	Kim算法^[10]	11.641 7	4.116 7	23.163 6	0.768 1	79.576 6
	刘算法^[11]	13.156 0	2.935 5	35.343 3	1.873 8	54.430 2
	本文算法	14.974 4	4.846 3	26.071 3	0.760 2	81.383 2
E2	原始图像	13.212 2	3.843 6	—	1.741 4	44.075 4
	He算法^[7]	14.781 8	6.420 3	24.200 1	0.749 9	51.237 1
	Kim算法^[10]	13.719 7	6.038 4	25.274 1	0.856 5	63.087 5
	刘算法^[11]	13.648 0	3.959 5	37.919 8	1.644 1	44.780 5
	本文算法	14.913 9	6.597 0	26.482 5	0.528 1	65.589 6
E3	原始图像	10.693 7	3.469 8	—	2.181 4	50.887 0
	He算法^[7]	11.863 2	5.262 3	24.084 5	0.744 7	45.300 1
	Kim算法^[10]	12.108 4	5.338 7	25.670 9	0.838 1	65.266 6
	刘算法^[11]	11.076 4	3.576 7	33.060 1	2.081 9	51.540 2
	本文算法	12.861 7	5.386 7	26.276 1	0.632 0	67.045 0
E4	原始图像	12.283 6	1.323 8	—	4.135 6	50.069 3
	He算法^[7]	14.207 5	3.277 0	17.995 6	0.966 2	50.141 4
	Kim算法^[10]	12.504 7	3.151 3	18.171 8	0.749 6	79.225 2
	刘算法^[11]	12.950 1	1.407 7	32.636 9	3.794 3	51.810 8
	本文算法	14.391 8	3.597 8	21.098 0	0.724 5	79.482 0

PL资源	可用资源	占用资源	占用率/%
LUT	56 200	19 143	34.1
FF	106 400	19 353	18.2
Block RAM	140	62	44.3
DSP	220	137	62.2

智能行车记录仪图像去雾系统的FPGA设计

FPGA Design of Image Defogging System in Intelligent Tachograph

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