Dept. of Electr. and Electron. Eng., Bogazici Univ., Istanbul.
IEEE Trans Image Process. 1993;2(4):523-8. doi: 10.1109/83.242361.
The method presented by T. Katayama and T. Hirai (1990), who considered the problem of semicausal autoregressive (AR) parameter identification for images degraded by observation noise, is extended. In particular, an approach to identifying both the causal and semicausal AR parameters without a priori knowledge of the observation noise power is proposed. The image is decomposed into 1-D independent complex scalar subsystems resulting from the vector state-space model, using the unitary discrete Fourier transform (DFT). Then the expectation-maximization algorithm is applied to each subsystem to identify the AR parameters of the transformed image. The AR parameters of the original image are then identified using the least-square method. The restored image is obtained as a byproduct of the EM algorithm.
本文扩展了 T. Katayama 和 T. Hirai（1990）提出的方法，他们考虑了观察噪声退化图像的半因果自回归（AR）参数识别问题。特别是，提出了一种在没有观察噪声功率先验知识的情况下同时识别因果和半因果 AR 参数的方法。通过使用幺正离散傅里叶变换（DFT），将向量状态空间模型分解为 1-D 独立复标量子系统。然后，将期望最大化算法应用于每个子系统，以识别变换图像的 AR 参数。最后，使用最小二乘法识别原始图像的 AR 参数。恢复的图像是 EM 算法的副产品。
