Communication Dans Un Congrès Année : 2019

Tensor-Factorization-Based 3D Single Image Super-Resolution with Semi-Blind Point Spread Function Estimation

Résumé

A volumetric non-blind single image super-resolution technique using tensor factorization has been recently introduced by our group. That method allowed a 2-order-of-magnitude faster high-resolution image reconstruction with equivalent image quality compared to state-of-the-art algorithms. In this work a joint alternating recovery of the high-resolution image and of the unknown point spread function parameters is proposed. The method is evaluated on dental computed tomography images. The algorithm was compared to an existing 3D super-resolution method using low-rank and total variation regularization, combined with the same alternating PSF-optimization. The two algorithms have shown similar improvement in PSNR, but our method converged roughly 40 times faster, under 6 minutes both in simulation and on experimental dental computed tomography data

Fichier principal
Vignette du fichier
hatvani_26210.pdf (465.61 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

hal-02884917 , version 1 (30-06-2020)

Licence

Identifiants

Citer

Janka Hatvani, Adrian Basarab, Jérôme Michetti, Miklós Gyöngy, Denis Kouamé, et al.. Tensor-Factorization-Based 3D Single Image Super-Resolution with Semi-Blind Point Spread Function Estimation. IEEE International Conference on Image Processing (ICIP 2019), Sep 2019, Taipei, Taiwan. pp.2871-2875, ⟨10.1109/ICIP.2019.8803354⟩. ⟨hal-02884917⟩
134 Consultations
213 Téléchargements

Altmetric

Partager

  • More