Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2013.420
Title: Fast sparsity-based orthogonal dictionary learning for image restoration
Authors: Bao, C.
Cai, J.-F.
Ji, H. 
Keywords: dictionary learning
image restoration
sparse representation
Issue Date: 2013
Citation: Bao, C., Cai, J.-F., Ji, H. (2013). Fast sparsity-based orthogonal dictionary learning for image restoration. Proceedings of the IEEE International Conference on Computer Vision : 3384-3391. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2013.420
Abstract: 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. © 2013 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/104563
ISBN: 9781479928392
DOI: 10.1109/ICCV.2013.420
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