Dept. of Electr. Eng., Houston Univ., TX.
IEEE Trans Image Process. 1995;4(8):1084-95. doi: 10.1109/83.403415.
A computationally efficient, easily implementable algorithm for MAP restoration of images degraded by blur and additive correlated Gaussian noise using Gibbs prior density functions is derived. This algorithm is valid for a variety of complete data spaces. The constraints upon the complete data space arising from the Gaussian image formation model are analyzed and a motivation is provided for the choice of the complete data, based upon the ease of computation of the resulting EM algorithms. The overlooked role of the null space of the blur operator in image restoration is introduced. An examination of this role reveals an important drawback to the use of the simulated annealing algorithm in maximizing a specific class of functionals. An alternative iterative method for computing the nullspace component of a vector is given. The ability of a simple Gibbs prior density function to enable partial recovery of the component of an image within the nullspace of the blur operator is demonstrated.
推导出一种使用吉布斯先验密度函数对因模糊和加性相关高斯噪声而退化的图像进行 MAP 恢复的计算效率高、易于实现的算法。该算法适用于各种完全数据空间。分析了高斯图像形成模型对完全数据空间的约束，并根据所得到的 EM 算法的计算简便性，为完全数据的选择提供了依据。引入了在图像恢复中模糊算子零空间被忽略的作用。对该作用的研究揭示了在最大化特定类函数时使用模拟退火算法的一个重要缺陷。给出了一种计算向量零空间分量的替代迭代方法。演示了简单吉布斯先验密度函数能够使模糊算子零空间内的图像分量部分恢复的能力。
