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Blind Single Image Super-Resolution via Iterated Shared Prior Learning
Ist Teil von
Pattern Recognition, p.151-165
Ort / Verlag
Cham: Springer International Publishing
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
In this paper, we adapt shared prior learning for blind single image super-resolution (SISR). From a variational perspective, we are aiming at minimizing an energy functional consisting of a learned data fidelity term and a data-driven prior, where the learnable parameters are computed in a mean-field optimal control problem. In the associated loss functional, we combine a supervised loss evaluated on synthesized observations and an unsupervised Wasserstein loss for real observations, in which local statistics of images with different resolutions are compared. In shared prior learning, only the parameters of the prior are shared among both loss functions. The kernel estimate is updated iteratively after each step of shared prior learning. In numerous numerical experiments, we achieve state-of-the-art results for blind SISR with a low number of learnable parameters and small training sets to account for real applications.