Abstract：
:Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Li J,Liu Zdoi
10.1038/s41598-019-47564-zsubject
Has Abstractpub_date
2019-08-05 00:00:00pages
11319issue
1issn
2045-2322pii
10.1038/s41598-019-47564-zjournal_volume
9pub_type
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