Abstract
We propose an automatic parameter selection strategy for variational image super-resolution of blurred and down-sampled images corrupted by additive white Gaussian noise (AWGN) with unknown standard deviation. By exploiting particular properties of the operators describing the problem in the frequency domain, our strategy selects the optimal parameter as the one optimising a suitable residual whiteness measure. Numerical tests show the effectiveness of the proposed strategy for generalised \(\ell _2\)-\(\ell _2\) Tikhonov problems.
LC acknowledges the support received by the EU H2020 RISE NoMADS, GA 777826. Research of AL, MP and FS was supported by ex60 project by the University of Bologna. All the authors acknowledge the “National Group for Scientific Computation (GNCS-INDAM)”.
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Pragliola, M., Calatroni, L., Lanza, A., Sgallari, F. (2021). Residual Whiteness Principle for Automatic Parameter Selection in \(\ell _2\)-\(\ell _2\) Image Super-Resolution Problems. 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_38
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