基于稀疏优化的烟羽断层重建方法

HFCAS OpenIR

	基于稀疏优化的烟羽断层重建方法
其他题名	Tomographic reconstruction of stack plume basedon sparse optimization
	2019-01-01
发表期刊	物理学报
ISSN	1000-3290
摘要	烟羽断层重建一般使用两台光谱仪采集数据,属于典型的不完全角度重建.为了提高重建结果的稳定性和接近度,将压缩感知理论引入气体分布重建领域.提出了一种新的计算机层析算法--低三阶导数全变分法,用于重建电厂烟囱排放的SO2截面的二维分布.使用低三阶导数模型模拟气体扩散,认为气体浓度对位置的三阶导数是稀疏的.将重建图像的全变分作为目标函数,并通过数值最优化方法求得气体浓度分布的最优解.数值模拟的结果表明,与传统的低三阶导数法相比,低三阶导数全变分法将接近度提高了80%以上.外场实验表明,重建图像的一致性相关因子达0.9023.低三阶导数全变分法能有效消除测量误差对图像重建的影响,提高重建图像的质量.
其他摘要	In this paper, we present a novel method of computing tomography, i.e. the low third deviation total variation(LTD-TV) method to reconstruct the two-dimensional distribution of SO2 of stack plume. The pathintegral data of the plume are collected by only two imaging differential absorption spectrometers(IDOASs).However, due to the insufficient number of IDOASs, conventional reconstruction methods result in severe streaking artifacts. The traditional low third derivative method is widely used to reconstruct the gas distribution. It suggests a spatial distribution of gas concentrations, which has a low third spatial derivative in every direction and at every point. The derivatives are usually set to be zero. The method improves the reconstructed images by providing extra information which contains the gas concentration in line with the distribution of the second order polynomial, but it also gives rise to the extra artifacts. To address this issue, we further improve the traditional low third deviation(LTD) method by suggesting that the third derivative of gas concentration is sparse. We therefore adopt the compressed sensing(CS) based total variation(TV)optimization framework. In the LTD-TV method, a logarithmic barrier function with TV is used as an objective function. The objective function is then optimized by numerical optimization method, in which the gradient projection is used to determine its descent direction and a Barzilai-Borwein scheme to determine its step-size.The final results are obtained by iterative optimization. Numerical simulations are performed to simulate the reconstruction of gas distribution which is in line with Gaussian distribution. Compared with the conventional LTD method, the LTD-TV method enhances the proximity by 20%-80%, and greatly corrects the artifacts near the edges of images. The result of field campaign suggests that concordance correlation factor between the collected data and reconstructed image is 0.9023. It also shows that it has good noise immunity. In summary, it is the first time that we have introduced the CS theory into the field of gas plume reconstruction. Compared with the existing methods, the LTD-TV method can greatly reduce the artifacts and increase the credibility of the reconstruction.
关键词	大气吸收光谱烟羽重建低三阶导数全变分法成像差分吸收光谱仪
收录类别	CSCD
语种	中文
CSCD记录号	CSCD:6566870
引用统计
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/67534
专题	中国科学院合肥物质科学研究院
推荐引用方式 GB/T 7714	. 基于稀疏优化的烟羽断层重建方法[J]. 物理学报,2019,068.
APA	(2019).基于稀疏优化的烟羽断层重建方法.物理学报,068.
MLA	"基于稀疏优化的烟羽断层重建方法".物理学报 068(2019).