Morphological Component Analysis: An Adaptive Thresholding Strategy
By Jérôme Bobin, Jean-Luc Starck, Mohamed-Jalal Fadili, Yassir Moudden, David L. Donoho. IEEE Transactions on Image Processing, 2007
331 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/3128f7e041c168219be8021a44604f5bf85fed4f
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In Computer Vision and Pattern Recognition
- Vishal M. Patel
- Lei Zhang
- David Dapeng Zhang
- Xiangchu Feng
- He Zhang
- Vishwanath A. Sindagi
- Ming-Hsuan Yang
- Meng Yang
In Media Technology
- Lei Zhang
- David Dapeng Zhang
- Xiangchu Feng
- Jianwei Ma
- Vishal M. Patel
- Gerlind Plonka-Hoch
- Meng Yang
- Christian Jutten
Citations in the corpus
- Upper Limits on the 21 cm Epoch of Reionization Power Spectrum from One Night with LOFAR (2017, methodology): “We note that GMCA does not remove most of the remaining side-lobe noise.”
- Image De-Raining Using a Conditional Generative Adversarial Network (2017, methodology): “Existing methods for solving (1), such as sparse codingbased methods [3], [4], [10], [53], neural network-based methods [54] and CNN-based methods [1] have all adopted a symmetric (encoding-decoding) structure.”
- Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, background): “It is also possible to select the best atom at each step according to other criteria [68, 27, 104], or to select several atoms at a time [79, 14].”
- The Curvelet Transform (2010, methodology): “[63], [3] applied curvelets for morphological component analysis.”
- Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, methodology): “2008), morphological component analysis (Bobin et al. 2007) and compressed sensing (Candes 2006).”