Experimental verification of three-dimensional temperature field reconstruction method based on Lucy-Richardson and nearest neighbor filtering joint deconvolution algorithm for flame light field imaging

Li, Tianjiao; Yuan, Yuan; Zhang, Biao; Sun, Jun; Xu, Chen; Shuai, Yong; Tan, He‐Ping

doi:10.1016/j.applthermaleng.2019.114235

Cited by 21 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the three-channel method is effective in obtaining the spatial distribution of the thermal field, and the solution results are consistent with the actual situation, as shown in Figure 4 (b). The solution temperature of the flame belly position is higher, which is the inevitable result of radiation temperature measurement [16][17][18]. Because the flame is a three-dimensional translucent system, the optical thickness of the flame belly position is relatively high, and the radiation received by the pixels in the corresponding position is the spatial superposition of each point in the flame belly position optical thickness, thus showing a higher radiation temperature.…”

Section: Verification Experimentsmentioning

confidence: 99%

Three-channel wide-spectrum temperature measurement technique

Yu,

Gu,

Dong

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

A theoretical and experimental study of a three-channel wide-spectrum temperature measurement technique is reported. The correctness of the application of the Taylor series to the temperature measurement integral equations is verified theoretically, and the effect of spectral emissivity on the approximation of the Taylor series is discussed. In the simulation results, the insensitivity of the optimal expansion wavelength to the spectral emissivity is revealed and applied as an important basis in the temperature solution method. Temperature measurement experiments using the two-color method and three-channel wide spectrum method were carried out. The theoretical correctness of the three-channel wide-spectrum temperature measurement method and the spatial solution ability of the temperature field are verified.

show abstract

Section: Verification Experimentsmentioning

confidence: 99%

Three-channel wide-spectrum temperature measurement technique

Yu,

Gu,

Dong

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In this radiation-based method, the reconstruction technique was developed on the backward Monte-Carlo methods. The reconstruction matrix equations are solved using the LSQR method ( Li et al, 2019 ). The acoustic temperature field reconstruction method is based on the principle that the propagation velocity of the sound wave is different at different temperatures.…”

Section: Introductionmentioning

confidence: 99%

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Zhao

Cao

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error.

show abstract

“…According to whether the blur kernel is known, it can be generally divided into two types: non-blind deblurring and blind deblurring. Early researches [3] mostly focused on non-blind deblurring, such as the Lucy-Richardson algorithm, which is based on Bayesian theory and assumes that the blur image satisfies the Poisson distribution, and deblurring by iteratively seeking maximum likelihood, however, due to the interference of noise factors, a reasonable number of iterations is the key to determine the quality of the restored image.…”

Section: Introductionmentioning

confidence: 99%

Image blind deblurring networks with back-projection feature fusion

Kong

Xue

et al. 2023

SIViP

View full text Add to dashboard Cite

： Aiming at the problem of image motion blur caused by hand-held camera jitter and object motion in the process of collecting photos, a generative adversarial network (GAN) based on feature fusion of back projection is proposed for blind image deblurring. Firstly, the generator network is established by using U-Net structure, and a feature fusion residual block based on back projection is designed according to the error feedback principle, which solves the problem of saving spatial information in U-Net structure. Secondly, the self-attention module is introduced into the generator network to extract the feature map that pays more attention to detail. Finally, the combination of perceptual loss, mean square error loss and relative generative adversarial loss effectively alleviates the mode collapse problem of traditional GAN and improves the stability of model training. The experimental results show that the peak signal to noise ratio (PSNR) and structural similarity (SSIM) of this method on GoPro data set are 30.183dB and 0.941 respectively, and 26.962 and 0.837 on the Kohler dataset, with the shortest running time, which are better than the existing mainstream methods. The restored image is clearer in subjective vision and richer in texture details, which can effectively improve the image deblurring effect.

show abstract

Experimental verification of three-dimensional temperature field reconstruction method based on Lucy-Richardson and nearest neighbor filtering joint deconvolution algorithm for flame light field imaging

Cited by 21 publications

References 38 publications

Three-channel wide-spectrum temperature measurement technique

Three-channel wide-spectrum temperature measurement technique

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Image blind deblurring networks with back-projection feature fusion

Contact Info

Product

Resources

About