IEEE Trans Image Process. 2017 Dec;26(12):5730-5742. doi: 10.1109/TIP.2017.2740566. Epub 2017 Aug 16.
A novel multi-morphological representation model for solving the nonlocal similarity-based image reconstruction from compressed measurements is introduced in this paper. Under the probabilistic framework, the proposed approach provides the nonlocal similarity clustering for image patches by using the Gaussian mixture models, and endows a multi-morphological representation for image patches in each cluster by using the Gaussians that represent the different features to model the morphological components. Using the simple alternating iteration, the developed piecewise morphological diversity estimation (PMDE) algorithm can effectively estimate the MAP of morphological components, thus resulting in the nonlinear estimation for image patches. We extend the PMDE to a piecewise morphological diversity sparse estimation by using the constrained Gaussians with the low-rank covariance matrices, to gain the performance improvements. We report the experimental results on image compressed sensing in the case of sensing nonoverlapping patches with Gaussian random matrices. The results demonstrate that our algorithms can suppress undesirable block artifacts efficiently, and delivers reconstructed images with higher qualities than other state-of-the-art methods.
本文提出了一种新的基于多形态表示的模型，用于解决基于非局部相似性的压缩测量图像重建问题。在概率框架下，该方法通过使用高斯混合模型对图像块进行非局部相似聚类，并使用表示不同特征的高斯函数为每个聚类中的图像块赋予多形态表示，以对形态成分进行建模。通过简单的交替迭代，所提出的分段形态多样性估计（PMDE）算法可以有效地估计形态成分的最大后验（MAP），从而实现对图像块的非线性估计。我们通过使用具有低秩协方差矩阵的约束高斯函数，将 PMDE 扩展到分段形态多样性稀疏估计，以获得性能提升。我们在使用高斯随机矩阵进行非重叠块传感的情况下，对图像压缩传感进行了实验。结果表明，我们的算法能够有效地抑制不需要的块状伪影，并且重建的图像质量优于其他最新方法。
