PCA Gaussianization for image processing

Authors:
Valero Laparra;Gustavo Camps-Valls;Jesús Malo
Affiliations:
Image Processing Laboratory, Universitat de València, Paterna, València, Spain;Image Processing Laboratory, Universitat de València, Paterna, València, Spain;Image Processing Laboratory, Universitat de València, Paterna, València, Spain
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

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.