Residual Whiteness Principle for Automatic Parameter Selection in ℓ2 - ℓ2 Image Super-Resolution Problems - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Residual Whiteness Principle for Automatic Parameter Selection in ℓ2 - ℓ2 Image Super-Resolution Problems

Résumé

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 ℓ2 - ℓ2 Tikhonov problems.

Dates et versions

hal-03453681 , version 1 (28-11-2021)

Identifiants

Citer

Luca Calatroni, Monica Pragliola, Alessandro Lanza, Fiorella Sgallari. Residual Whiteness Principle for Automatic Parameter Selection in ℓ2 - ℓ2 Image Super-Resolution Problems. SSVM 2021 - 8th International Conference on Scale Space and Variational Methods in Computer Vision, May 2021, Virtual Event, France. pp.476-488, ⟨10.1007/978-3-030-75549-2_38⟩. ⟨hal-03453681⟩
40 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More