Abstract
Obtaining high resolution images from low resolution data with clipped noise is algorithmically challenging due to the ill-posed nature of the problem. So far such problems have hardly been tackled, and the few existing approaches use simplistic regularisers. We show the usefulness of two adaptive regularisers based on anisotropic diffusion ideas: Apart from evaluating the classical edge-enhancing anisotropic diffusion regulariser, we introduce a novel non-local one with one-sided differences and superior performance. It is termed sector diffusion. We combine it with all six variants of the classical super-resolution observational model that arise from permutations of its three operators for warping, blurring, and downsampling. Surprisingly, the evaluation in a practically relevant noisy scenario produces a different ranking than the one in the noise-free setting in our previous work (SSVM 2017).
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Acknowledgements
J.W. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 741215, ERC Advanced Grant INCOVID). We thank our colleagues Dr. Matthias Augustin and Dr. Pascal Peter for useful comments on a draft version of the paper.
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Bodduna, K., Weickert, J., Cárdenas, M. (2021). Multi-frame Super-Resolution from Noisy Data. In: Elmoataz, A., Fadili, J., Quéau, Y., Rabin, J., Simon, L. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science(), vol 12679. Springer, Cham. https://doi.org/10.1007/978-3-030-75549-2_45
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