A bimodal co-sparse analysis model for image processing
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Publication:1799985
DOI10.1007/s11263-014-0786-5zbMath1398.94038arXiv1406.6538OpenAlexW1973839740MaRDI QIDQ1799985
Simon Hawe, Tim Habigt, Martin Kleinsteuber, Martin Kiechle
Publication date: 19 October 2018
Published in: International Journal of Computer Vision (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1406.6538
analysis operator learningbimodal image reconstructionbimodal image registrationco-sparse analysis modelgradient methods on manifolds
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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- Bipartite entanglement in systems of identical particles: the partial transposition criterion
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Algorithms for simultaneous sparse approximation. I: Greedy pursuit
- The cosparse analysis model and algorithms
- Learning low-level vision
- On Single Image Scale-Up Using Sparse-Representations
- Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
- Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model
- Learning Sparsifying Transforms
- Insights Into Analysis Operator Learning: From Patch-Based Sparse Models to Higher Order MRFs
- Analysis versus synthesis in signal priors
- Image Super-Resolution Via Sparse Representation
- Analysis Operator Learning and its Application to Image Reconstruction
- An efficient hybrid conjugate gradient method for unconstrained optimization
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