Adaptive example-based super-resolution using kernel PCA with a novel classification approach

Takahiro Ogawa,Miki Haseyama

doi:10.1186/1687-6180-2011-138

Takahiro Ogawa, Miki Haseyama

Open Access

https://doi.org/10.1186/1687-6180-2011-138

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Abstract

An adaptive example-based super-resolution (SR) using kernel principal component analysis (PCA) with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution (LR) images, the proposed method performs clustering of high-resolution (HR) patches clipped from training HR images in advance. Based on two nonlinear eigenspaces, respectively, generated from HR patches and their corresponding low-frequency components in each cluster, an inverse map, which can estimate missing high-frequency components from only the known low-frequency components, is derived. Furthermore, by monitoring errors caused in the above estimation process, the proposed method enables adaptive selection of the optimal cluster for each target local patch, and this corresponds to the novel classification approach in our method. Then, by combining the above two approaches, the proposed method can adaptively estimate the missing high-frequency components, and successful reconstruction of the HR image is realized.

Highlights

In the field of image processing, high-resolution images are needed for various fundamental applications such as surveillance, high-definition TV and medical image processing [1]
We calculate a nonlinear eigenspace for each cluster and enable the modeling of the elements belonging to each cluster by its nonlinear eigenspace. Based on these nonlinear eigenspaces, the proposed method can perform the clustering of training high resolution (HR) patches in this subsection and the high-frequency component estimation, which simultaneously realizes the classification of target patches for realizing the adaptive reconstruction, in the following subsection
The method [27] is a representative method which utilizes Kernel principal component analysis (PCA) (KPCA) for performing the superresolution, and its improvement is achieved by utilizing the classification scheme in [8]

Summary

Introduction

In the field of image processing, high-resolution images are needed for various fundamental applications such as surveillance, high-definition TV and medical image processing [1]. Based on these nonlinear eigenspaces, the proposed method can perform the clustering of training HR patches in this subsection and the high-frequency component estimation, which simultaneously realizes the classification of target patches for realizing the adaptive reconstruction, in the following subsection. The proposed method can provide the optimal estimation results if the target local patches can be represented in the obtained eigenspaces, correctly.

Results

Conclusion