TitleSparse Hierarchical Nonparametric Bayesian learning for light field representation and denoising
Publication TypeConference Paper
Year of Publication2016
AuthorsSun, X, Meng, N, Xu, Z, Lam, EY, So, HK-H
Conference Name2016 International Joint Conference on Neural Networks (IJCNN)2016 International Joint Conference on Neural Networks (IJCNN)
Conference LocationVancouver, BC, Canada

In this paper, we present a sparse hierarchical non-parametric Bayesian (SHNB) model, which is used to represent the data captured by the light field cameras. Specifically, a light field can be represented as a set of sub-aperture views. In order to capture the visual variations of these viewpoints, we propose the so-called “depth flow” features. Then based on the depth flow features, we model these views statistically with a sparse representation in a fully unsupervised manner. While local dictionaries are learned based on each sub-aperture view, all the views with different perspectives share one global dictionary. To show the effectiveness of the proposed model, we apply our model to denoise the light field data. In the experiments, we demonstrate that our method outperforms several state-of-the-art light field denoising approaches.


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