Direct superresolution for realistic image reconstruction.

Traditional superresolution techniques employ optimizers, priors, and regularizers to deliver stable, appealing restorations even though deviating from the real, ground-truth scene. We have developed a non-regularized superresolution algorithm that directly solves a fully-characterized multi-shift imaging reconstruction problem to achieve realistic restorations without being penalized by improper assumptions made in the inverse problem. An adaptive frequency-based filtering scheme is introduced to upper bound the reconstruction errors while still producing more fine details as compared with previous methods when inaccurate shift estimation, noise, and blurring scenarios are considered.