High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks
Nature Publishing Group UK
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Li, J., & Liu, Z. (2019). High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks. Scientific Reports, 9 (1)https://doi.org/10.1038/s41598-019-47564-z
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.
Article, /639/624/1107/510, /639/166/984, /134, /123, article
External DOI: https://doi.org/10.1038/s41598-019-47564-z
This record's URL: https://www.repository.cam.ac.uk/handle/1810/308764