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Title: Learnable-pattern scanning based deep compressed imaging
We present a new deep compressed imaging modality by scanning a learned illumination pattern on the sample and detecting the signal with a single-pixel detector. This new imaging modality allows a compressed sampling of the object, and thus a high imaging speed. The object is reconstructed through a deep neural network inspired by compressed sensing algorithm. We optimize the illumination pattern and the image reconstruction network by training an end-to-end auto-encoder framework. Comparing with the conventional single-pixel camera and point-scanning imaging system, we accomplish a high-speed imaging with a reduced light dosage, while preserving a high imaging quality.  more » « less
Award ID(s):
1847141
PAR ID:
10268494
Author(s) / Creator(s):
; ;
Editor(s):
Goda, Keisuke; Tsia, Kevin K.
Date Published:
Journal Name:
High-Speed Biomedical Imaging and Spectroscopy VI
Volume:
11654
Page Range / eLocation ID:
1165413
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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