PCA Gaussianization for image processing

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PCA Gaussianization for image processing
The estimation of high-dimensional probability density functions (PDFs) is not an easy task for many image processing applications. The linear models assumed by widely used transforms are often quite restrictive to describe the PDF of natural images. In fact, additional non-linear processing is needed to overcome the limitations of the model. On the contrary, the class of techniques collectively known as projection pursuit, which solve the high-dimensional problem by sequential univariate solutions, may be applied to very general PDFs (e.g. iterative Gaussianization procedures). However, the associated computational cost has prevented their extensive use in image processing. In this work, we propose a fast alternative to iterative Gaussianization methods that makes it suitable for image processing while ensuring its theoretical convergence. Method performance is successfully illustrated in image synthesis and classification problems.
Valero Laparra, Gustavo Camps-Valls, Jesús
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICIP
Authors Valero Laparra, Gustavo Camps-Valls, Jesús Malo
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