Comparison of Dictionary-Based Image Reconstruction Algorithms for Inverse Problems
| Autor: | Didem Dogan, Figen S. Oktem |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Iterative reconstruction Inverse problem Regularization (mathematics) 030218 nuclear medicine & medical imaging Spectral imaging Convolution 03 medical and health sciences 0302 clinical medicine Signal-to-noise ratio Prior probability 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Convex function Algorithm Sparse matrix |
| Zdroj: | SIU |
| Popis: | Many inverse problems in imaging involve measurements that are in the form of convolutions. Sparsity priors are widely exploited in their solutions for regularization as these problems are generally ill-posed. In this work, we develop image reconstruction methods for these inverse problems using patchbased and convolutional sparse models. The resulting regularized inverse problems are solved via the alternating direction method of multipliers (ADMM). The performance of the developed algorithms is investigated for an application in computational spectral imaging. Simulation results suggest that the convolutional sparse model provides similar reconstruction performance with the patch-based model; but the convolutional method is more advantageous in terms of computational cost. |
| Databáze: | OpenAIRE |
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