• Chinese Journal of Lasers
  • Vol. 46, Issue 3, 0309001 (2019)
Jun Shao1、2、*, Jingfeng Ye2, Sheng Wang2, Zhiyun Hu2, Bolang Fang2, Zhenrong Zhang2, and Jingyin Li1
Author Affiliations
  • 1 School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
  • 2 The State Key Laboratory of Laser Interaction with Matter, Northwest Institute of Nuclear Technology, Xi'an, Shaanxi 710024, China
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    DOI: 10.3788/CJL201946.0309001 Cite this Article Set citation alerts
    Jun Shao, Jingfeng Ye, Sheng Wang, Zhiyun Hu, Bolang Fang, Zhenrong Zhang, Jingyin Li. Background Noise Suppress Method for Hydroxyl Tagging Velocimetry in Combustion Flow Field[J]. Chinese Journal of Lasers, 2019, 46(3): 0309001 Copy Citation Text show less
    HTV experimental images. (a) OHB fluorescence interference; (b) flow field noise; (c) system noise
    Fig. 1. HTV experimental images. (a) OHB fluorescence interference; (b) flow field noise; (c) system noise
    Background noise. (a) Schematic of experimental setup; (b) experimental results
    Fig. 2. Background noise. (a) Schematic of experimental setup; (b) experimental results
    Contrast of background noise in and out of PLIF display sheet
    Fig. 3. Contrast of background noise in and out of PLIF display sheet
    Simulation images with background noise. (a) Simulation model with odd inflection points; (b) simulation model with even inflection points
    Fig. 4. Simulation images with background noise. (a) Simulation model with odd inflection points; (b) simulation model with even inflection points
    Background noise histogram image
    Fig. 5. Background noise histogram image
    Standard deviation versus gain
    Fig. 6. Standard deviation versus gain
    Contrast of simulation results and experimental results. (a) Simulation results; (b) experimental results
    Fig. 7. Contrast of simulation results and experimental results. (a) Simulation results; (b) experimental results
    Contrast of de-noising results of different parameters of simulation models. (a) Contrast of de-noising results with different window numbers with odd number of inflection points; (b) contrast of de-noising results with different window numbers with even number of inflection points; (c) evaluation of de-noising results of different signal models; (d) evaluation of de-noising results with different window sizes
    Fig. 8. Contrast of de-noising results of different parameters of simulation models. (a) Contrast of de-noising results with different window numbers with odd number of inflection points; (b) contrast of de-noising results with different window numbers with even number of inflection points; (c) evaluation of de-noising results of different signal models; (d) evaluation of de-noising results with different window sizes
    De-noising results of noise model. (a) Noise model; (b) ROI; (c) Hough detection; (d) partition filtering
    Fig. 9. De-noising results of noise model. (a) Noise model; (b) ROI; (c) Hough detection; (d) partition filtering
    De-noising analysis of wavelet transform. (a)Model; (b)noised model; (c) RPSNR versus wavelet threshold coefficient; (d) RPSNR versus wavelet decomposition layer; (e) RPSNR versus wavelet iterated time; (f) RPSNR 、RSNR of de-noised signal versus RSNR of noised signal; (g) wavelet transform processing
    Fig. 10. De-noising analysis of wavelet transform. (a)Model; (b)noised model; (c) RPSNR versus wavelet threshold coefficient; (d) RPSNR versus wavelet decomposition layer; (e) RPSNR versus wavelet iterated time; (f) RPSNR 、RSNR of de-noised signal versus RSNR of noised signal; (g) wavelet transform processing
    Flow diagram of image preprocessing
    Fig. 11. Flow diagram of image preprocessing
    Contrast of simulation de-noising results. (a) Noised model; (b) Gaussian smoothing; (c) median filtering; (d) Laplace sharpening; (e) spatial filtering; (f) proposed method
    Fig. 12. Contrast of simulation de-noising results. (a) Noised model; (b) Gaussian smoothing; (c) median filtering; (d) Laplace sharpening; (e) spatial filtering; (f) proposed method
    Experimental locale photo
    Fig. 13. Experimental locale photo
    De-noising results of experimental data. (a) Experimental image; (b) proposed method result; (c) Gaussian smoothing result; (d) median filtering result; (e) Laplace sharpening result
    Fig. 14. De-noising results of experimental data. (a) Experimental image; (b) proposed method result; (c) Gaussian smoothing result; (d) median filtering result; (e) Laplace sharpening result
    Noise typeOHB interferenceFlow field noiseSystem noise
    Morphological characteristicConcentratedDiscreteDiscrete
    PowerHighest, concentratedHigher, dispersedHigh, dispersed
    Position spaceIn PLIF display sheet,uncovered the signalDistributed throughout theimage, especially in thePLIF display sheetDistributed throughoutthe image
    Existing reasonOccured in areas of combustionor chemical reactionsCaused by scattering of the flowfield wall or other particlesExisted in allexperimental images
    Table 1. Characteristic analysis of background noise
    MethodSpatial filter based on Hough transformWavelet transform
    FeatureLess calculation, can segment differentmorphology distribution imagesCan remove Gaussian noise of image
    Applied objectStray light in flow field and uncoveredsignal combustion interferenceSensor noise including physical noise,random noise and so on
    DisadvantageNot suitable for suppressing background ofsignal covered by interferenceNot suitable for interference and noisefrom combustion flow field
    SolutionWhen removing background noise,combines with wavelet transformOptimizes spatial filter
    Table 2. Background noise processing methods
    MethodRPSNR/dBRSNR/dBRMSE/dBRPDE/%
    Original image11.91210.66264.1867×10327.12
    Gaussian smoothing14.02401.26962.5744×10326.45
    Median filtering13.71751.21292.7627×10326.84
    Laplace sharpening13.02691.44073.2388×10326.97
    Spatial filtering25.96059.4139164.82780.846
    Spatial filtering & wavelet function28.701414.568387.68800.711
    Table 3. Contrast of de-noising results
    Jun Shao, Jingfeng Ye, Sheng Wang, Zhiyun Hu, Bolang Fang, Zhenrong Zhang, Jingyin Li. Background Noise Suppress Method for Hydroxyl Tagging Velocimetry in Combustion Flow Field[J]. Chinese Journal of Lasers, 2019, 46(3): 0309001
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