非线性自适应迭代重建算法用于相干层析抗噪仿真  

Simulation on anti-noise performance of nonlinear auto-adjusting iterative reconstruction technique used in interferometric tomo graphy

作　　者：曲培树[1] 宋一中[1] 

机构地区：[1]德州学院物理系,山东德州253000

出　　处：《光电子．激光》2013年第8期1644-1650,共7页Journal of Optoelectronics·Laser

基　　金：国家自然科学基金(11203006);山东省自然科学基金(ZR2009AM021)资助项目

摘　　要：为进一步完善非线性自适应迭代重建算法(NAIRT)并将其可靠地应用于实际流场层析诊断,仿真研究了NAIRT的抗噪性能。采用仿真技术,模拟含跳变的复杂空气流场,相干投影,获得Radon变换投影数据。采用RandG函数模拟产生Gauss随机噪声,噪声的相对强度通过信噪比(SNR)标征,设置强度不同的SNR,获得相应的随机噪声。随机噪声与投影数据线性叠加,获得含噪投影。采用NAIRT逆投影重建,重建结果与无噪重建结果比较,并用均方差(MSE)对重建结果定量分析。结果发现,在60dB SNR噪声情况下,NAIRT能够比较精确重建仿真流场,MSE降到1.82×10-5;10dB SNR时,NAIRT仍能重建出仿真场的轮廓,MSE稳定在4.91×10-4;100dB SNR时,NAIRT能够十分精确地重建仿真流场,MSE衰减到1.26×10-6,逼近无噪重建效果。重建图像和误差分析都表明,NAIRT具有十分优越的抗噪性能。In order to further improve nonlinear auto-adjusting iterative reconstruction technique （NAIRT） and reliably apply it to diagnose actual air flow field, the anti-noise performance of the NAIRT is specially investigated by simulations. A complicated air flow field is simulated with transition object. It is projected by interferometric tomography. The real projections are obtained by Radon transformation. Gaussian random noise signals are simulated to be produced with function RandG. The noise＇s relative intensity is expressed with the ratio of signal to noise （S/N）. A series of corresponding Gaussian random noise signals are produced with different intensity levels by simple calculation. The projections containing noises are obtained from the real projections added with noise linearly. The inverse projection reconstruc- tions are implemented with NAIRT. The reconstructed results are analyzed in mean square error （MSE） index and compared with the result reconstructed with clean projections under the same conditions. At the noise level of 60 dB S/N,NAIRT could reconstruct the model by a decent accuracy. The MSE dec- lins to 1.82X 10-5 after about 40 iteration cycles. At the noise level of 10 dB S/N, NAIRT could still reconstruct the model by the level of the profile. The MSE is stabilized at 4. 91 ）〈 10 4 after about 40 it- eration cycles. At the noise level of 100 dB S/N,NAIRT could accurately reconstruct the model by mi- cro-detail. The MSE declines to 1.26× 10^-6 at the end of 103 iteration cycles. The reconstructed result is very close to that reconstructed with clean projections. Both the reconstructed images and MSE analyses demonstrate that NAIRT has wonderful anti-noise performance.

关 键 词：非线性自适应迭代重建算法(NAIRT) 抗噪性能 随机噪声 信噪比(SNR) 投影 重建 

分 类 号：O438[机械工程—光学工程]