基于图像的启发式三维火焰重建算法

研究了火焰的重建,为改进现有稀疏视角输入下的层析成像算法的重建精度,提出了一种基于图像的启发式火焰重建方法。该重建方法通过迭代优化求解能量约束模型,实现对火焰三维温度场的重建。算法允许复杂的成像模型应用于重建过程中,使渲染过程更接近火焰真实成像过程,并对数据平滑性进行约束,以提高重建结果质量。算法在迭代过程中使用启发式规则引导温度场数据调整,使算法快速收敛,并通过使用图形处理器加速重建过程。使用模拟数据和真实捕获数据进行实验,结果表明,与现有方法相比,提出的方法有效提高了重建结果的精度及平滑性,验证了提出方法的有效性。

Heuristic 3D flame reconstruction from images

The flame reconstruction was studied, and an image-based heuristic 3D flame reconstruction approach was proposed to improve the reconstruction accuracy of the existing sparse-view tomography methods. The presented heuristic approach uses an energy constraint model to reconstruct 3D flame temperature. The approach allows complex image models to be applied to the reconstruction to make the rendering process closer to the real flame image formation process. Through the smoothness constraints to data, the reconstruction results were optimized. During the iterative process of the reconstruction, heuristic rules and GPU (graphics processing unit) were exploited to accelerate the reconstruction. The outcome of the experiments on both simulated data and captured data showed that, the proposed approach enhanced the precision and smoothness of the reconstruction compared with the existing methods.