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Table of Contents
1. An Overview of SaT Segmentation Methodology and Its Applications in Image Processing
2. Analysis of different losses for deep learning image colorization
3. Blind phase retrieval with fast algorithms
4. Bregman Methods for Large-Scale Optimisation with Applications in Imaging
5. Connecting Hamilton-Jacobi Partial Differential Equations with Maximum a Posteriori and Posterior Mean Estimators for Some Non-convex Priors
6. Convex non-Convex Variational Models
7. Data-Informed Regularization for Inverse and Imaging Problems
8. Diffraction Tomography, Fourier Reconstruction, and Full Waveform Inversion
9. Domain Decomposition for Non-smooth (in Particular TV) Minimization
10. Fast numerical methods for image segmentation models.
2. Analysis of different losses for deep learning image colorization
3. Blind phase retrieval with fast algorithms
4. Bregman Methods for Large-Scale Optimisation with Applications in Imaging
5. Connecting Hamilton-Jacobi Partial Differential Equations with Maximum a Posteriori and Posterior Mean Estimators for Some Non-convex Priors
6. Convex non-Convex Variational Models
7. Data-Informed Regularization for Inverse and Imaging Problems
8. Diffraction Tomography, Fourier Reconstruction, and Full Waveform Inversion
9. Domain Decomposition for Non-smooth (in Particular TV) Minimization
10. Fast numerical methods for image segmentation models.