Production Engineering Research Laboratory, Hitachi, Ltd., Yokohama, 244-0817, Japan.
IEEE Trans Image Process. 2003;12(9):1044-53. doi: 10.1109/TIP.2003.816007.
In this paper, learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
本文提出了基于学习的图像恢复和盲图像恢复算法。这些算法通过利用从相似图像中学到的先验知识，与该领域的传统方法有所不同。原始图像及其通过已知退化算子（恢复问题）退化的版本被用于设计 VQ 码本。码向量是使用模糊图像设计的。对于每个这样的向量，也可以从原始图像中获得高频信息。在恢复过程中，根据码本，从给定的退化图像的低频信息中估计给定的高频信息。对于盲恢复问题，针对各种模糊函数版本设计了多个码本。给定一幅有噪和模糊的图像，根据相似性度量选择一个码本，从而提供模糊的识别。为了使恢复过程具有计算效率，利用主成分分析（PCA）和 VQ-最近邻方法。给出了仿真结果，以验证所提出算法的有效性。
