Deep Learning for Computational Imaging
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portes grátis
Deep Learning for Computational Imaging
Heckel, Prof Reinhard
Oxford University Press
04/2025
224
Mole
9780198947189
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
1: Introduction
2: Solving inverse problems with optimization tasks
3: Solving optimization problems
4: Sparse modelling
5: Plug-and-play methods
6: Learning to solve inverse problems end-to-end
7: Unrolled neural networks
8: Self-supervised learning
9: Signal reconstruction via imposing generative priors
10: Diffusion models
11: Signal reconstruction with un-trained neural networks
12: Coordinate-based multi-layer perceptrons
13: Robustness to perturbations
14: Datasets and evaluation of image reconstruction methods
15: Advanced reconstruction problems
16: Mathematical background
2: Solving inverse problems with optimization tasks
3: Solving optimization problems
4: Sparse modelling
5: Plug-and-play methods
6: Learning to solve inverse problems end-to-end
7: Unrolled neural networks
8: Self-supervised learning
9: Signal reconstruction via imposing generative priors
10: Diffusion models
11: Signal reconstruction with un-trained neural networks
12: Coordinate-based multi-layer perceptrons
13: Robustness to perturbations
14: Datasets and evaluation of image reconstruction methods
15: Advanced reconstruction problems
16: Mathematical background
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
1: Introduction
2: Solving inverse problems with optimization tasks
3: Solving optimization problems
4: Sparse modelling
5: Plug-and-play methods
6: Learning to solve inverse problems end-to-end
7: Unrolled neural networks
8: Self-supervised learning
9: Signal reconstruction via imposing generative priors
10: Diffusion models
11: Signal reconstruction with un-trained neural networks
12: Coordinate-based multi-layer perceptrons
13: Robustness to perturbations
14: Datasets and evaluation of image reconstruction methods
15: Advanced reconstruction problems
16: Mathematical background
2: Solving inverse problems with optimization tasks
3: Solving optimization problems
4: Sparse modelling
5: Plug-and-play methods
6: Learning to solve inverse problems end-to-end
7: Unrolled neural networks
8: Self-supervised learning
9: Signal reconstruction via imposing generative priors
10: Diffusion models
11: Signal reconstruction with un-trained neural networks
12: Coordinate-based multi-layer perceptrons
13: Robustness to perturbations
14: Datasets and evaluation of image reconstruction methods
15: Advanced reconstruction problems
16: Mathematical background
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
