Matches in SemOpenAlex for { <https://semopenalex.org/work/W3025836064> ?p ?o ?g. }
- W3025836064 abstract "We introduce a vortex phase transform with a lenslet-array to accompany shallow, dense, ``small-brain'' neural networks for high-speed and low-light imaging. Our single-shot ptychographic approach exploits the coherent diffraction, compact representation, and edge enhancement of Fourier-tranformed spiral-phase gradients. With vortex spatial encoding, a small brain is trained to deconvolve images at rates 5-20 times faster than those achieved with random encoding schemes, where greater advantages are gained in the presence of noise. Once trained, the small brain reconstructs an object from intensity-only data, solving an inverse mapping without performing iterations on each image and without deep-learning schemes. With this hybrid, optical-digital, vortex Fourier encoded, small-brain scheme, we reconstruct MNIST Fashion objects illuminated with low-light flux (5 nJ/cm$^2$) at a rate of several thousand frames per second on a 15 W central processing unit, two orders of magnitude faster than convolutional neural networks." @default.
- W3025836064 created "2020-05-21" @default.
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- W3025836064 date "2020-05-15" @default.
- W3025836064 modified "2023-09-27" @default.
- W3025836064 title "Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders" @default.
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