Identification and restoration of the turbulence degraded images based on the parametric estimation
The point spread function (PSF) of the turbulence degrading system, as well as the variance of the observation noise and the model of the original image, are unknown a priori in practical imaging processes when the aero-optic effect exists. Both of the PSF and the variance of the observation noise have to be identified from the turbulence degraded images before restoring them. An approach of identification and restoration for the turbulence degraded images based on the parameter estimation are proposed. The turbulence degraded image is expressed as an autoregressive moving average (ARMA) process. The maximum likelihood (ML) approach is used to the identification of the ARMA parameters. The expectation maximization (EM) algorithm is employed to optimize the nonlinear likelihood function in an efficient way. This identification and restoration algorithm is used to the wind tunnel experimenting images, and the experimental results show that the restoration effect are improved obviously. The true image and the PSF are identified in the process of identifying the ARMA parameters.
ARMA model parameter estimation restoration algorithm EM algorithm maximum likelihood estimate.
LI Dongxing ZHAO Yan XU Dong
School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and
国际会议
北京
英文
2007-08-05（万方平台首次上网日期，不代表论文的发表时间）