基于ZYNQ的燃煤火焰图像硬件去雾研究与实现

发布时间：2018-04-27 16:42

  本文选题：燃煤火焰图像 + 去雾　； 参考：《湖南大学》2016年硕士论文

【摘要】：燃煤窑炉是一类典型的复杂工业被控对象,通过分析窑炉内燃煤火焰视频图像,可以快速、直观获取炉内工况特征信息,对于窑炉的优化控制与节能减排有着重要意义。然而由于窑炉内工况复杂,粉尘极大,导致窑内图像模糊不清,这对图像特征提取和人工判别都有很大影响,因此对燃煤火焰图像进行快速去粉尘预处理有着重要的现实应用意义。本文分析了窑炉火焰与大气散射物理模型的关联性,采用暗原色先验去雾算法用于窑炉火焰图像增强。针对基于大气散射物理模型的去雾算法在目前处理器架构下时间复杂度过高,无法实现较高分辨率图像实时处理的问题,本文采用具有ARM(Advanced RISC Machines)硬核的ZYNQ-7000系列(简称ZYNQ)现场可编程门阵列(Field-Programmable Gate Array, FPGA),开发了一套基于FPGA的燃煤火焰图像去雾增强算法的硬件实现系统。围绕ZYNQ系列FPGA芯片,主要研究内容如下。(1)从物理模型入手分析燃煤火焰图像与户外带雾图像降质共同点,提出采用暗原色先验去雾算法用于燃煤火焰图像增强。针对已有的暗原色去雾算法在处理燃煤火焰图像中出现的问题,提出一组经验参数,用于修正环境光与透射率的估计值,实验证明改进后的算法获得较好的处理效果。(2)分析暗原色先验去雾算法的时空复杂度及其透射图估计、暗通道计算等模块的时序关系,利用Vivado HLS(High Level Synthesis)高层次综合工具实现暗原色先验去雾算法中各模块的硬件化,生成燃煤火焰图像去雾IP核(Intellectual Property Core),设计了片上系统的硬件架构,最终构建了一套基于ZYNQ系列的燃煤火焰图像硬件去雾系统。(3)最后,论文对该硬件去雾系统进行了试验验证,单位性能功耗较其他方法降低了2个数量级,处理速度约是PC平台10倍,ARM平台的60倍,GPU平台的5倍。
[Abstract]:Coal-fired kiln is a kind of typical complex industrial controlled object. By analyzing the video image of coal-fired flame in kiln, the characteristic information of working condition can be obtained quickly and intuitively, which is of great significance for optimizing control of kiln and saving energy and emission reduction. However, because of the complex working conditions and the heavy dust in the kiln, the images in the kiln are blurred, which has a great influence on image feature extraction and manual discrimination. Therefore, it has important practical significance to preprocess coal-fired flame images with fast dust removal. In this paper, the correlation between furnace flame and atmospheric scattering physical model is analyzed, and a dark priori de-fogging algorithm is used to enhance the flame image of kiln. In order to solve the problem that the time complexity of the defog algorithm based on atmospheric scattering physical model is too high under the current processor architecture, it can not realize the high resolution image processing in real time. In this paper, the field programmable gate array Field-Programmable Gate Array (FPGA) with ARM(Advanced RISC machines hard core is used to develop a hardware implementation system based on FPGA for image de-fogging enhancement of coal-fired flame images. Focusing on ZYNQ series FPGA chips, the main research contents are as follows: 1) based on the physical model, this paper analyzes the common features of coal-fired flame images and outdoor images with fog, and proposes a dark priori de-fogging algorithm for coal-fired flame image enhancement. In order to solve the problem of dark primary color de-fogging algorithm in dealing with coal-fired flame images, a set of empirical parameters is proposed to correct the estimation of ambient light and transmittance. The experimental results show that the improved algorithm has a better processing effect. (2) analyzing the temporal and spatial complexity of dark primary color priori de-fogging algorithm and the temporal relationships of transmission image estimation, dark channel calculation and other modules, etc. The hardware of each module in the dark primary color priori de-fogging algorithm is realized by using the Vivado HLS(High Level synthesis high-level synthesis tool. The IP core of de-fogging is generated from the coal-fired flame image, and the hardware architecture of the on-chip system is designed. Finally, a set of coal-fired flame image hardware de-fogging system based on ZYNQ series is constructed. Finally, the hardware de-fogging system is tested and verified in this paper. The unit performance power consumption is reduced by two orders of magnitude compared with other methods. The processing speed is about 10 times that of PC platform and 60 times of that of arm platform and 5 times that of GPU platform.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41;TK175
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本文编号：1811502