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

Janka Hatvani (1, 2) , Adrian Basarab (2, 3) , Jérôme Michetti (2) , Miklós Gyöngy (1) , Denis Kouamé (2) , Janka Hatvani

1 Pázmány Péter Catholic University
2 IRIT-MINDS - CoMputational imagINg anD viSion
3 UT3 - Université Toulouse III - Paul Sabatier

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

Mots clés: Image enhancement; 3D super-resolution; Semi-blind deconvolution; 3D computed tomography

Domaines: Traitement des images [eess.IV]

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⟩