Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration
Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration
复制标题
DOI：
10.1109/tip.2016.2616286
复制
发表时间：
2016-12
影响因子：
10.6
通讯作者：
Takahiro Ogawa;M. Haseyama
中科院分区：
文献类型：
--
作者：
Takahiro Ogawa;M. Haseyama
本文提出了一种基于自适应子空间的多子分割逆投影算法（ASIP-DIMS），用于丢失图像数据的恢复。在该方法中，估计丢失的图像数据的目标问题被划分为多个子问题，并与其他已知的图像数据的约束迭代求解每个子问题。通过投影到图像块的子空间模型中，计算每个子问题的解，为了简单起见，我们称这个过程为“基于子空间的逆投影”。所提出的方法可以使用更高的维度的子空间在每个子问题中找到唯一的解决方案，成功的恢复变得可行，因为可以保留高水平的图像表示性能。这是本文最大的贡献。此外，所提出的方法产生几个子空间从已知的训练样本，并使一个新的标准在上述框架中的推导，以自适应地选择最佳的子空间为每个目标补丁。该方法利用ASIP-DIMS实现了丢失图像数据的恢复。由于我们的方法可以估计任何类型的丢失的图像数据，它的潜力在两个图像恢复任务，图像修复和超分辨率，基于多变量分析的几种方法，本文也显示。
This paper presents adaptive subspace-based inverse projections via division into multiple sub-problems (ASIP-DIMSs) for missing image data restoration. In the proposed method, a target problem for estimating missing image data is divided into multiple sub-problems, and each sub-problem is iteratively solved with the constraints of other known image data. By projection into a subspace model of image patches, the solution of each sub-problem is calculated, where we call this procedure “subspace-based inverse projection” for simplicity. The proposed method can use higher dimensional subspaces for finding unique solutions in each sub-problem, and successful restoration becomes feasible, since a high level of image representation performance can be preserved. This is the biggest contribution of this paper. Furthermore, the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch. In this way, the proposed method realizes missing image data restoration using ASIP-DIMS. Since our method can estimate any kind of missing image data, its potential in two image restoration tasks, image inpainting and super-resolution, based on several methods for multivariate analysis is also shown in this paper.