Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation
Neural Information Processing
Morphological Diversity and Sparsity for Multichannel Data Restoration
Journal of Mathematical Imaging and Vision
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Image restoration through L0 analysis-based sparse optimization in tight frames
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image restoration by mixture modelling of an overcomplete linear representation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic aurora images classification algorithm based on separated texture
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Null space pursuit: an operator-based approach to adaptive signal separation
IEEE Transactions on Signal Processing
The role of sparse data representation in semantic image understanding
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Learning the Morphological Diversity
SIAM Journal on Imaging Sciences
Wave-pattern processing towards inverse reliability problems
Proceedings of the 2011 Emerging M&S Applications in Industry and Academia Symposium
Beyond sparsity: The role of L1-optimizer in pattern classification
Pattern Recognition
Face hallucination based on morphological component analysis
Signal Processing
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In a recent paper, a method called morphological component analysis (MCA) has been proposed to separate the texture from the natural part in images. MCA relies on an iterative thresholding algorithm, using a threshold which decreases linearly towards zero along the iterations. This paper shows how the MCA convergence can be drastically improved using the mutual incoherence of the dictionaries associated to the different components. This modified MCA algorithm is then compared to basis pursuit, and experiments show that MCA and BP solutions are similar in terms of sparsity, as measured by the lscr1 norm, but MCA is much faster and gives us the possibility of handling large scale data sets.