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In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution and denoising performance are comparable to existing methods.
Index Terms- Principal component analysis, kernel methods, image models, image enhancement, unsupervised learning.

B. Sch?lkopf, K. I. Kim and M. O. Franz, "Iterative Kernel Principal Component Analysis for Image Modeling," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. , pp. 1351-1366, 2005.
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