Open AccessRotated rectangular aperture imaging through multi-frame blind deconvolution with Hyper-Laplacian priors
Author(s) -
Hao Zhou,
Yueting Chen,
Hao Feng,
Guomian Lv,
Zhihai Xu,
Qi Li
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.424129
Subject(s) - deblurring , deconvolution , computer science , blind deconvolution , prior probability , aperture (computer memory) , optics , computer vision , artificial intelligence , frame (networking) , aperture synthesis , image restoration , algorithm , image processing , image (mathematics) , physics , bayesian probability , acoustics , interferometry , telecommunications
Rotated rectangular aperture imaging has many advantages in large aperture telephoto systems due to its lower cost and lower complexity. This technology makes it possible to build super large aperture telescopes. In this paper, we combine the ideas of deblurring with rotated rectangular aperture imaging and propose an image synthesis algorithm based on multi-frame deconvolution. In the specific reconstruction process, Hyper-Laplacian priors and sparse priors are used, and an effective solution is developed. The simulation and real shooting experiments show that our algorithm has excellent performance in visual effect and objective evaluation. The synthetic images are significantly sharper than the results of the existing methods.