Dept. of Electr. Eng., Colorado State Univ., Ft. Collins, CO.
IEEE Trans Image Process. 1992;1(4):488-95. doi: 10.1109/83.199918.
A two-dimensional method which uses a full-plane image model to generate a more accurate filtered estimate of an image that has been corrupted by additive noise and full-plane blur is presented. Causality is maintained within the filtering process by using multiple concurrent block estimators. In addition, true state dynamics are preserved, resulting in an accurate Kalman gain matrix. Simulation results on a test image corrupted by additive white Gaussian noise are presented for various image models and compared to those of the previous block Kalman filtering methods.
提出了一种二维方法，该方法使用全平面图像模型生成已被加性噪声和全平面模糊损坏的图像的更准确的滤波估计。通过使用多个并发块估计器，在滤波过程中保持因果关系。此外，真实状态动态得以保留，从而得到准确的卡尔曼增益矩阵。针对各种图像模型，对一个被加性高斯白噪声污染的测试图像进行了仿真，并与以前的块卡尔曼滤波方法的结果进行了比较。
