Journal ArticleDOI
Coupled dictionary learning method for image decomposition
TLDR
A novel variational model for image decomposition and a new cartoon-texture dictionary learning algorithm, which is guided by diffusion flow, which has better performance than the existing algorithms in image decomposing and denoising are presented.Abstract:
A novel variational model for image decomposition is proposed. Meanwhile a new cartoon-texture dictionary learning algorithm, which is guided by diffusion flow, is presented. Numerical experiments show that the proposed method has better performance than the existing algorithms in image decomposition and denoising.read more
Citations
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Journal ArticleDOI
Lightness biased cartoon-and-texture decomposition for textile image segmentation
TL;DR: Experimental results demonstrate that the proposed model can generate better segmentation results for textile images than classical FMF based models, and restrain the estimated image regions from degeneration.
Journal ArticleDOI
CT and MRI image fusion based on multiscale decomposition method and hybrid approach
TL;DR: The proposed CT and MRI image fusion based on multiscale decomposition method and hybrid approach outperforms the state-of-the-art method SR and NSCT in terms of visual effect and objective quality.
Journal ArticleDOI
A novel variational model for image decomposition
TL;DR: One coupled variational model for image decomposition is proposed, which contains first- and second-order regularization terms which can remove the noise better and the solution for the introduced vector field is just given Gaussian convolution.
Journal ArticleDOI
Adaptive variational models for image decomposition
TL;DR: This paper proposes two new variational models for image decomposition, one of which is the cartoon component, consisting only of geometric structure, and the other is the oscillatory components, consisting of texture.
Proceedings ArticleDOI
Coupled dictionary learning for multimodal data: An application to concurrent intracranial and scalp EEG
Loukianos Spyrou,Saeid Sanei +1 more
TL;DR: This paper employs a concurrent intracranial and scalp EEG dataset, to learn a dictionary and a mapping function between the two modalities, and develops an algorithm that obtains an optimal coupled dictionary, sparse coefficients and the mapping functionbetween modalities.
References
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Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Journal ArticleDOI
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Michael Elad,Michal Aharon +1 more
TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.
Proceedings ArticleDOI
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TL;DR: A modification to the matching pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual at every step and thereby leads to improved convergence is proposed.
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Sparse Representation for Color Image Restoration
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