Abstract
In this paper, we present a novel method for multi-frame superresolution (SR). Our main goal is to improve the spatial resolution of a multi-line scan camera for an industrial inspection task. High resolution output images are reconstructed using our proposed SR algorithm for multi-channel data, which is based on the trainable reaction-diffusion model. As this is a supervised learning approach, we simulate ground truth data for a real imaging scenario. We show that learning a regularizer for the SR problem improves the reconstruction results compared to an iterative reconstruction algorithm using TV or TGV regularization. We test the learned regularizer, trained on simulated data, on images acquired with the real camera setup and achieve excellent results.
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Acknowledgements
We acknowledge grant support from the FWF START project BIVISION, No. Y729, the ERC starting grant HOMOVIS, No. 640156 and from the AIT and the Austrian Federal Ministry of Science under the HRSM programme BGBl. II Nr. 292/2012.
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Klatzer, T., Soukup, D., Kobler, E., Hammernik, K., Pock, T. (2017). Trainable Regularization for Multi-frame Superresolution. In: Roth, V., Vetter, T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science(), vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_8
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DOI: https://doi.org/10.1007/978-3-319-66709-6_8
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