Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
IEEE Trans Image Process. 2011 Feb;20(2):417-32. doi: 10.1109/TIP.2010.2070072. Epub 2010 Aug 26.
A missing intensity interpolation method using a kernel principal component analysis (PCA)-based projection onto convex sets (POCS) algorithm and its applications are presented in this paper. In order to interpolate missing intensities within a target image, the proposed method reconstructs local textures containing the missing pixels by using the POCS algorithm. In this reconstruction process, a nonlinear eigenspace is constructed from each kind of texture, and the optimal subspace for the target local texture is introduced into the constraint of the POCS algorithm. In the proposed method, the optimal subspace can be selected by monitoring errors converged in the reconstruction process. This approach provides a solution to the problem in conventional methods of not being able to effectively perform adaptive reconstruction of the target textures due to missing intensities, and successful interpolation of the missing intensities by the proposed method can be realized. Furthermore, since our method can restore any images including arbitrary-shaped missing areas, its potential in two image reconstruction tasks, image enlargement and missing area restoration, is also shown in this paper.
本文提出了一种基于核主成分分析（PCA）投影到凸集（POCS）算法的缺失强度插值方法及其应用。为了在目标图像内插值缺失的强度，所提出的方法使用 POCS 算法重建包含缺失像素的局部纹理。在这个重建过程中，从每种纹理中构建一个非线性特征空间，并将最优子空间引入到 POCS 算法的约束中。在所提出的方法中，可以通过监控重建过程中收敛的误差来选择最优子空间。这种方法解决了传统方法中由于缺失强度而无法有效地对目标纹理进行自适应重建的问题，并且可以通过所提出的方法成功地插值缺失的强度。此外，由于我们的方法可以恢复包括任意形状缺失区域的任何图像，因此在两个图像重建任务（图像放大和缺失区域恢复）中也展示了其潜力。
