open access

https://hdl.handle.net/2115/52942

POCS-Based Texture Reconstruction Method Using Clustering Scheme by Kernel PCA

英語

POCS

2007-08-01

Institute of Electronics, Information and Communication Engineers

IEICE transactions on fundamentals of electronics, communications and computer sciences

E90-A(8)

1519-1527

A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

学術雑誌論文

Version of Record

copyright©2007 IEICE

0916-8508

1745-1337

AA10826239