Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders
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| Format: | Preprint |
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2020
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| _version_ | 1866911026653429760 |
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| author | Muminov, Baurzhan Vuong, Luat T. |
| author_facet | Muminov, Baurzhan Vuong, Luat T. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2005_07682 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders Muminov, Baurzhan Vuong, Luat T. Image and Video Processing Computer Vision and Pattern Recognition Optics 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. |
| title | Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2005.07682 |