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

Tensor-Factorization-Based 3D Single Image Super-Resolution with Semi-Blind Point Spread Function Estimation (regular paper). Hatvani, J., Basarab, A., Michetti, J., Gyongy, M., & Kouamé, D. In IEEE International Conference on Image Processing (ICIP 2019), Taipei, Taiwan, 22/09/19-25/09/19, pages (on line), http://www.ieee.org/, 2019. IEEE : Institute of Electrical and Electronics Engineers.

Paper abstract bibtex

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.

@InProceedings{ HaBaMiGyKo2019.1,
author = {Hatvani, Janka and Basarab, Adrian and Michetti, J\'er�me and Gyongy, Mikl�s and Kouam\'e, Denis},
title = "{Tensor-Factorization-Based 3D Single Image Super-Resolution with Semi-Blind Point Spread Function Estimation (regular paper)}",
booktitle = "{IEEE International Conference on Image Processing (ICIP 2019), Taipei, Taiwan, 22/09/19-25/09/19}",
year = {2019},
publisher = {IEEE : Institute of Electrical and Electronics Engineers},
address = {http://www.ieee.org/},
pages = {(on line)},
language = {anglais},
URL = {https://doi.org/10.1109/ICIP.2019.8803354 - https://oatao.univ-toulouse.fr/26210/},
abstract = {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.}
}

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