Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)

By Michael Elad, Jean-Luc Starck, P. Querre, David L. Donoho. 2005

969 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c77338d031d41c3c5ae027e64a64242555b3ec2d

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Living on the edge: phase transitions in convex programs with random data (2013, methodology): “This approach for demixing sparse signals is sometimes called morphological component analysis [16, 40,88,89].”
Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity (2010, background): “The PLE inpainting is compared with a number of recent method s, including “MCA” (morphological component analysis) [24], “ASR” (adaptive sparse reconstr uctions) [33] , “ECM” (expectation conditional maximization) [27] , “KR” (kernel regression) [60], “FOE” ( fields of experts) [56], “BP” (beta pro”
Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach (2013, background): “2 dB Inpainting performance comparison among several competing methods: exemplar-based [48], MCA-based [49], DA-based [44], and SAIST-based for six toy-example images (three regular textures and three regular edges). respectively.”
Mask-Specific Inpainting with Deep Neural Networks (2014, background): “(ii) The second class is based on sparse representations using dictionaries [9, 14, 11].”
Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing (2009, neutral): “This scheme again presents the ‘recipe ingredients without recipe amounts’ obstacle: users may be turned off by the requirement to specify many such tunable parameters.”
Image Inpainting : Overview and Recent Advances (2014, background): “The authors in [58] propose an algorithm based on this sparsity seeking image separation into two components.”
Sparse Representation for Color Image Restoration (2008, background): “Indeed, these various transforms have led to highly effective algorithms in many applications in image processing, such as compression [14], de-noising [15]–[18], inpainting [19], and more.”
A full sky, low foreground, high resolution CMB map from WMAP (2008, methodology): “Equivalent in spirit although quite different in implementation is the use of an “inpainting” method as that of Elad et al. (2005).”
Non-parametric seismic data recovery with curvelet frames (2008, methodology): “Following the inpainting approach by Elad et al. (2005) based on redundant transforms, we formulate seismic data regularization as a curvelet-based norm-one recovery problem.”
Wave atoms and sparsity of oscillatory patterns (2007, neutral): “domain [10,18,32].”
Sparsity- and continuity-promoting seismic image recovery with curvelet frames (2008, background): “Following [23], the above constrained optimization problem (P1), is replaced by a series of simpler unconstrained optimization problems”
DEVELOPMENT OF THE MODEL OF GALACTIC INTERSTELLAR EMISSION FOR STANDARD POINT-SOURCE ANALYSIS OF FERMI LARGE AREA TELESCOPE DATA (2016, methodology): “We first filtered out IR point sources present in the dust map and applied inpainting methods based on wavelet decomposition (Elad et al. 2005) to reconstruct the signal at the point-source locations.”
Analysis versus synthesis in signal priors (2006, methodology): “One specific option is to approximate the synthesis-based method by an analysis-based one, as is done in [6] where the analysis operator is taken as the pseudo-inverse of the synthesis dictionary.”
Sparse Representation for Signal Classification (2006, methodology): “In [18], missing pixels in images are successfully recovered by inpainting method based on sparse representation.”
Compressed Sensing (2012, methodology): “This minimization problem was first considered in [31] for inpainting – a terminology used for recovery of missing data in imaging science.”
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2009, methodology): “An appealing image processing application that relies on MCA is inpainting, where missing pixels in an image are ﬁlled in, based on a sparse representation of the existing pixels [68].”