Missing Intensity Interpolation Using a Kernel PCA-Based POCS Algorithm and its Applications

• Ogawa, Takahiro

• Haseyama, Miki

抄録

A missing intensity interpolation method using a kernel 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.

収録刊行物

• IEEE Transactions on Image Processing

  IEEE Transactions on Image Processing 20 (2), 417-432, 2011-02

  IEEE - Institute of Electrical and Electronics Engineers

キーワード

• Image enlargement
• interpolation
• kernel principal component analysis (PCA)
• missing area restoration
• projection onto convex sets (POCS)

詳細情報

• CRID: 1050001339007196800
• NII論文ID: 120002795275
• DOI: 10.1109/tip.2010.2070072
• HANDLE: 2115/44871
• ISSN: 10577149
• Web Site: http://xplorestaging.ieee.org/ielx5/83/5688261/05557816.pdf?arnumber=5557816
• 本文言語コード: en
• 資料種別: journal article
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