
A SetTheoretic Study of the Relationships of Image Models and Priors for Restoration Problems
Image prior modeling is the key issue in image recovery, computational i...
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Improving Robustness of DeepLearningBased Image Reconstruction
Deeplearningbased methods for different applications have been shown v...
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Transform Learning for Magnetic Resonance Image Reconstruction: From Modelbased Learning to Building Neural Networks
Magnetic resonance imaging (MRI) is widely used in clinical practice for...
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GANbased Projector for Faster Recovery in Compressed Sensing with Convergence Guarantees
A Generative Adversarial Network (GAN) with generator G trained to model...
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The Power of Complementary Regularizers: Image Recovery via Transform Learning and LowRank Modeling
Recent works on adaptive sparse and on lowrank signal modeling have dem...
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Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
Multichannel blind deconvolution is the problem of recovering an unknown...
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Learning Filter Bank Sparsifying Transforms
Data is said to follow the transform (or analysis) sparsity model if it ...
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Optimal Sample Complexity for Stable Matrix Recovery
Tremendous efforts have been made to study the theoretical and algorithm...
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Blind Gain and Phase Calibration via Sparse Spectral Methods
Blind gain and phase calibration (BGPC) is a bilinear inverse problem in...
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VIDOSAT: Highdimensional Sparsifying Transform Learning for Online Video Denoising
Techniques exploiting the sparsity of images in a transform domain have ...
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Joint Dimensionality Reduction for Two Feature Vectors
Many machine learning problems, especially multimodal learning problems...
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DataDriven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing
Compressed sensing is a powerful tool in applications such as magnetic r...
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Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
Natural signals and images are wellknown to be approximately sparse in ...
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ℓ_0 Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees
Many applications in signal processing benefit from the sparsity of sign...
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Yoram Bresler
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