NGUYEN, Dang-Phuong-Lan; HERTRICH, Johannes; AUJOL, Jean-Francois; BERTHOUMIEU, Yannick

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NGUYEN, Dang-Phuong-Lan
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire de l'intégration, du matériau au système [IMS]

HERTRICH, Johannes
Technical University of Berlin / Technische Universität Berlin [TUB]

AUJOL, Jean-Francois
Institut de Mathématiques de Bordeaux [IMB]

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Document de travail - Pré-publication

Résumé en anglais

Single Image Super-Resolution algorithms based on patches have been noticed and widely used over the past decade. Recently, generalized Gaussian mixture models (GGMMs) have been shown to be a suitable tool for many image processing problems because of the flexible shape parameter. In this work, we first propose to use a joint GGMM learned from concatenated vectors of high-and low-resolution training patches. For each low-resolution patch, we compute the minimum mean square error (MMSE) estimator and generate the high-resolution image by averaging these estimates. We select the MMSE approach using GGMM as the method is invariant to affine contrast change and also invariant to a linear super-resolution operator. Unfortunately, the large dimension of the concatenated high-and low-resolution patches leads to instabilities and an intractable computational effort when estimating the parameters of the GGMM. Thus, we propose to combine a GGMM with a principal component analysis and derive an EM algorithm for estimating the parameters of the arising model. We demonstrate the performance of our model by numerical examples on synthetic and real images of materials' microstructures.< Réduire

Mots clés en anglais

Generalized Gaussian mixture model

image super-resolution

high-dimensional data

dimensionality reduction

Principal Component Analysis (PCA)

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Image super-resolution with PCA Reduced generalized Gaussian mixture models

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Résumé en anglais

Mots clés en anglais

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