Journal of Mathematical Imaging and Vision
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Comparing measures of sparsity
IEEE Transactions on Information Theory
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Double sparsity: learning sparse dictionaries for sparse signal approximation
IEEE Transactions on Signal Processing
Sparse representations for spatial prediction and texture refinement
Journal of Visual Communication and Image Representation
A novel predual dictionary learning algorithm
Journal of Visual Communication and Image Representation
An augmented Lagrangian approach to general dictionary learning for image denoising
Journal of Visual Communication and Image Representation
Dictionary learning for image prediction
Journal of Visual Communication and Image Representation
Online semi-supervised discriminative dictionary learning for sparse representation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Dictionary learning based impulse noise removal via L1-L1 minimization
Signal Processing
Online dictionary learning algorithm with periodic updates and its application to image denoising
Expert Systems with Applications: An International Journal
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Modeling signals by sparse and redundant representations has been drawing considerable attention in recent years. Coupled with the ability to train the dictionary using signal examples, these techniques have been shown to lead to state-of-the-art results in a series of recent applications. In this paper we propose a novel structure of such a model for representing image content. The new dictionary is itself a small image, such that every patch in it (in varying location and size) is a possible atom in the representation. We refer to this as the image-signature-dictionary (ISD) and show how it can be trained from image examples. This structure extends the well-known image and video epitomes, as introduced by Jojic, Frey, and Kannan [in Proceedings of the IEEE International Conference on Computer Vision, 2003, pp. 34-41] and Cheung, Frey, and Jojic [in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 42-49], by replacing a probabilistic averaging of patches with their sparse representations. The ISD enjoys several important features, such as shift and scale flexibilities, and smaller memory and computational requirements, compared to the classical dictionary approach. As a demonstration of these benefits, we present high-quality image denoising results based on this new model.