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

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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https://hal.archives-ouvertes.fr/hal-02884917
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Janka Hatvani, Adrian Basarab, Jérôme Michetti, Miklós Gyöngy, Denis Kouamé. 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⟩

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