Application of Landweber Method for Three-Dimensional Temperature Field Reconstruction Based on the Light-Field Imaging Technique

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Xing Huang ◽  
Hong Qi ◽  
Xiao-Luo Zhang ◽  
Ya-Tao Ren ◽  
Li-Ming Ruan ◽  
...  

Combined with the light-field imaging technique, the Landweber method is applied to the reconstruction of three-dimensional (3D) temperature distribution in absorbing media theoretically and experimentally. In the theoretical research, simulated exit radiation intensities on the boundary of absorbing media according to the computing model of light field are employed as inputs for inverse analysis. Compared with the commonly used iterative methods, i.e., the least-square QR decomposition method and algebraic reconstruction technique (ART), the Landweber method can produce reconstruction results with better quality and less computational time. Based on the numerical study, an experimental investigation is conducted to validate the suitability of the proposed method. The temperature distribution of the ethylene diffusion flame is reconstructed by using the Landweber method from the flame image captured by a light-field camera. Good agreement was found between the reconstructed temperature distribution and the measured temperature data obtained by a thermocouple. All the experimental results demonstrate that the temperature distribution of ethylene flame can be reconstructed reasonably by using the Landweber method combined with the light-field imaging technique, which is proven to have potential for the use in noncontract measurement of temperature distribution in practical engineering applications.

2020 ◽  
Vol 7 (03) ◽  
Author(s):  
Peter Quicke ◽  
Carmel L. Howe ◽  
Pingfan Song ◽  
Herman V. Jadan ◽  
Chenchen Song ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6061
Author(s):  
Lei Han ◽  
Xiaohua Huang ◽  
Zhan Shi ◽  
Shengnan Zheng

Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the balance between computational time and the accuracy of depth estimation is still worth exploring. The geometry in light field imaging is the basis of depth estimation, and the abundant light-field data provides convenience for applying deep learning algorithms. The Epipolar Plane Image (EPI) generated from the light-field data has a line texture containing geometric information. The slope of the line is proportional to the depth of the corresponding object. Considering the light field depth estimation as a spatial density prediction task, we design a convolutional neural network (ESTNet) to estimate the accurate depth quickly. Inspired by the strong image feature extraction ability of convolutional neural networks, especially for texture images, we propose to generate EPI synthetic images from light field data as the input of ESTNet to improve the effect of feature extraction and depth estimation. The architecture of ESTNet is characterized by three input streams, encoding-decoding structure, and skipconnections. The three input streams receive horizontal EPI synthetic image (EPIh), vertical EPI synthetic image (EPIv), and central view image (CV), respectively. EPIh and EPIv contain rich texture and depth cues, while CV provides pixel position association information. ESTNet consists of two stages: encoding and decoding. The encoding stage includes several convolution modules, and correspondingly, the decoding stage embodies some transposed convolution modules. In addition to the forward propagation of the network ESTNet, some skip-connections are added between the convolution module and the corresponding transposed convolution module to fuse the shallow local and deep semantic features. ESTNet is trained on one part of a synthetic light-field dataset and then tested on another part of the synthetic light-field dataset and real light-field dataset. Ablation experiments show that our ESTNet structure is reasonable. Experiments on the synthetic light-field dataset and real light-field dataset show that our ESTNet can balance the accuracy of depth estimation and computational time.


2020 ◽  
Vol 40 (1) ◽  
pp. 0111014
Author(s):  
刘慧芳 Liu Huifang ◽  
周骛 Zhou Wu ◽  
蔡小舒 Cai Xiaoshu ◽  
周雷 Zhou Lei ◽  
郭延昂 Guo Yan''ang

2020 ◽  
Vol 47 (12) ◽  
pp. 1204005
Author(s):  
伍俊龙 Wu Junlong ◽  
郭正华 Guo Zhenghua ◽  
陈先锋 Chen Xianfeng ◽  
马帅 Ma Shuai ◽  
晏旭 Yan Xu ◽  
...  

2019 ◽  
Vol 28 (05) ◽  
pp. 1
Author(s):  
Lei Wang ◽  
Qing Ye ◽  
Xianan Dou ◽  
Jinsong Nie ◽  
Xiaoquan Sun

2018 ◽  
Vol 26 (4) ◽  
pp. 4035 ◽  
Author(s):  
Zhaowei Xin ◽  
Dong Wei ◽  
Xingwang Xie ◽  
Mingce Chen ◽  
Xinyu Zhang ◽  
...  

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