Dept. of Electr. Eng., Auburn Univ., AL.
IEEE Trans Image Process. 1992;1(3):301-11. doi: 10.1109/83.148604.
The point spread function (PSF) of a blurred image is often unknown a priori; the blur must first be identified from the degraded image data before restoring the image. Generalized cross-validation (GCV) is introduced to address the blur identification problem. The GCV criterion identifies model parameters for the blur, the image, and the regularization parameter, providing all the information necessary to restore the image. Experiments are presented which show that GVC is capable of yielding good identification results. A comparison of the GCV criterion with maximum-likelihood (ML) estimation shows the GCV often outperforms ML in identifying the blur and image model parameters.
点扩散函数（PSF）的模糊图像通常是未知的先验；必须首先从退化的图像数据中识别模糊，然后再恢复图像。广义交叉验证（GCV）被引入以解决模糊识别问题。GCV 准则识别模糊、图像和正则化参数的模型参数，提供恢复图像所需的所有信息。实验表明，GCV 能够产生良好的识别结果。将 GCV 准则与最大似然（ML）估计进行比较表明，GCV 在识别模糊和图像模型参数方面通常优于 ML。
