Name
Abstract | ||
|---|---|---|
This article proposes a new method for image separation into a linear combination of morphological components. Sparsity in fixed dictionaries is used to extract the cartoon and oscillating content of the image. Complicated texture patterns are extracted by learning adapted local dictionaries that sparsify patches in the image. These fixed and learned sparsity priors define a nonconvex energy, and the separation is obtained as a stationary point of this energy. This variational optimization is extended to solve more general inverse problems such as inpainting. A new adaptive morphological component analysis algorithm is derived to find a stationary point of the energy. Using adapted dictionaries learned from data allows one to circumvent some difficulties faced by fixed dictionaries. Numerical results demonstrate that this adaptivity is indeed crucial in capturing complex texture patterns. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1137/090770783 | SIAM J. Imaging Sciences |
Keywords | Field | DocType |
adaptive morphological component analysis,dictionary learning,analysis algorithm,texture,fixed dictionary,cartoon images,morphological component,new adaptive morphological component,nonconvex energy,image separation,complicated texture pattern,morphological diversity,complex texture pattern,new method,inpainting,stationary point,wavelets.,sparsity,generalized inverse,image texture,oscillations,wavelets | Linear combination,Morphological component analysis,Pattern recognition,Inpainting,Stationary point,Inverse problem,Artificial intelligence,Image separation,Prior probability,Mathematics,Wavelet | Journal |
Volume | Issue | ISSN |
3 | 3 | 1936-4954 |
Citations | PageRank | References |
22 | 1.07 | 35 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Peyré | 1 | 287 | 17.29 |
| Jalal Fadili | 2 | 1184 | 80.08 |
| Jean-Luc Starck | 3 | 1183 | 122.27 |