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Internal Generative Mechanism Based Otsu Multilevel Thresholding Segmentation for Medical Brain Images Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Tuning Sparsity for Face Hallucination Representation

Authors : Zhongyuan Wang, Jing Xiao, Tao Lu, Zhenfeng Shao, Ruimin Hu

Published in: Advances in Multimedia Information Processing -- PCM 2015

Publisher: Springer International Publishing

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Abstract

Due to the under-sparsity or over-sparsity, the widely used regularization methods, such as ridge regression and sparse representation, lead to poor hallucination performance in the presence of noise. In addition, the regularized penalty function fails to consider the locality constraint within the observed image and training images, thus reducing the accuracy and stability of optimal solution. This paper proposes a locally weighted sparse regularization method by incorporating distance-inducing weights into the penalty function. This method accounts for heteroskedasticity of representation coefficients and can be theoretically justified from Bayesian inference perspective. Further, in terms of the reduced sparseness of noisy images, a moderately sparse regularization method with a mixture of ℓ1 and ℓ2 norms is introduced to deal with noise robust face hallucination. Various experimental results on public face database validate the effectiveness of proposed method.

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Metadata
Title
Tuning Sparsity for Face Hallucination Representation
Authors
Zhongyuan Wang
Jing Xiao
Tao Lu
Zhenfeng Shao
Ruimin Hu
Copyright Year
2015
Book
Advances in Multimedia Information Processing -- PCM 2015

Print ISBN: 978-3-319-24074-9

Electronic ISBN: 978-3-319-24075-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-24075-6_29