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Fast sparsity based orthogonal dictionary learning for image restoration

Chenglong Bao Department of Mathematics, National University of Singapore, Singapore,119076 Jian-Feng Cai Department of Mathematics, University of Iowa, Iowa City, IA, USA, 52242 Hui Ji Department of Mathematics, National University of Singapore, Singapore,119076

Machine Learning mathscidoc:2206.41010

ICCV, 2013.4
In recent years, how to learn a dictionary from input images for sparse modelling has been one very active topic in image processing and recognition. Most existing dictionary learning methods consider an over-complete dictionary, e.g. the K-SVD method. Often they require solving some minimization problem that is very challenging in terms of computational feasibility and efficiency. However, if the correlations among dictionary atoms are not well constrained, the redundancy of the dictionary does not necessarily improve the performance of sparse coding. This paper proposed a fast orthogonal dictionary learning method for sparse image representation. With comparable performance on several image restoration tasks, the proposed method is much more computationally efficient than the over-complete dictionary based learning methods.
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@inproceedings{chenglong2013fast,
  title={Fast sparsity based orthogonal dictionary learning for image restoration},
  author={Chenglong Bao, Jian-Feng Cai, and Hui Ji},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220616154930320737382},
  booktitle={ICCV},
  year={2013},
}
Chenglong Bao, Jian-Feng Cai, and Hui Ji. Fast sparsity based orthogonal dictionary learning for image restoration. 2013. In ICCV. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220616154930320737382.
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