Dept. of Electr. Eng. and Comput. Sci., Wisconsin Univ., Milwaukee, WI.
IEEE Trans Image Process. 1993;2(1):27-40. doi: 10.1109/83.210863.
A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF is a coupled one which provides continuity (inside regions of smooth gray tones) and discontinuity (at region boundaries) constraints for the restoration problem which is, in general, ill posed. The computational difficulty associated with the EM procedure for MRFs is resolved by using the mean field theory from statistical mechanics. An orthonormal blur decomposition is used to reduce the chances of undesirable locally optimal estimates. Experimental results on synthetic and real-world images show that this approach provides good blur estimates and restored images. The restored images are comparable to those obtained by a Wiener filter in mean-square error, but are most visually pleasing.
一种基于马尔可夫随机场（MRF）模型的 EM（期望最大化）方法被用来同时估计退化模型并恢复图像。该 MRF 是一个耦合的模型，它为恢复问题提供了连续性（在平滑灰度区域内）和不连续性（在区域边界处）的约束，而恢复问题通常是不适定的。通过使用统计力学中的平均场理论，解决了与 MRF 的 EM 过程相关的计算难题。使用正交模糊分解来减少不理想的局部最优估计的可能性。在合成和真实图像上的实验结果表明，这种方法可以提供良好的模糊估计和恢复图像。恢复的图像在均方误差上可与维纳滤波器获得的图像相媲美，但在视觉上更令人愉悦。
