Dept. of Electr. Eng. and Comput. Sci., Northwestern Univ., Evanston, IL.
IEEE Trans Image Process. 1995;4(9):1236-51. doi: 10.1109/83.413168.
We develop a recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and the intensity field from a noisy-blurred image sequence. Current motion-compensated spatio-temporal noise filters treat the estimation of the DVF as a preprocessing step. Generally, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, we establish a link between the two estimators. It is through this link that the DVF estimate and its corresponding accuracy information are shared with the other intensity estimator, and vice versa. To model the DVF and the intensity field, we use coupled Gauss-Markov (CGM) models. A CGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line field, which governs the transitions between the submodels. The CGM models are well suited for estimating the displacement and intensity fields since the resulting estimates preserve the boundaries between the stationary areas present in both fields. Detailed line fields are proposed for the modeling of these boundaries, which also take into account the correlations that exist between these two fields. A Kalman-type estimator results, followed by a decision criterion for choosing the appropriate set of line fields. Several experiments using noisy and noisy-blurred image sequences demonstrate the superior performance of the proposed algorithm with respect to prediction error and mean-square error.
我们开发了一种递归模型基最大后验（MAP）估计器，该估计器可以从噪声模糊图像序列中同时估计位移矢量场（DVF）和强度场。当前的运动补偿时空噪声滤波器将 DVF 的估计视为预处理步骤。通常，在将这些估计用于滤波器之前，不会尝试验证这些估计的准确性。通过同时估计这两个场，我们在两个估计器之间建立了联系。通过这个链接，将 DVF 估计及其相应的准确性信息与其他强度估计器共享，反之亦然。为了对 DVF 和强度场进行建模，我们使用耦合高斯-马尔可夫（CGM）模型。CGM 模型由两个级别组成：一个上层，由具有各种特性的几个子模型组成，以及一个下层或线场，用于控制子模型之间的转换。CGM 模型非常适合估计位移和强度场，因为所得的估计保留了这两个场中存在的静止区域之间的边界。为了对这些边界进行建模，提出了详细的线场，并考虑了这两个场之间存在的相关性。随后是一个用于选择适当的线场集的决策准则，得到一个卡尔曼型估计器。使用噪声和噪声模糊图像序列进行的多项实验表明，与预测误差和均方误差相比，所提出的算法具有优越的性能。
