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PhyCV: The First Physics-inspired Computer Vision Library

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arxiv 2301.12531 v2 pith:XKR2LOZJ submitted 2023-01-29 eess.IV cs.CV

classification eess.IVcs.CV
keywords algorithmsphycvphysicalcomputercomputingfirstlibraryphysics-inspired
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PhyCV is the first computer vision library which utilizes algorithms directly derived from the equations of physics governing physical phenomena. The algorithms appearing in the current release emulate, in a metaphoric sense, the propagation of light through a physical medium with natural and engineered diffractive properties followed by coherent detection. Unlike traditional algorithms that are a sequence of hand-crafted empirical rules or deep learning algorithms that are usually data-driven and computationally heavy, physics-inspired algorithms leverage physical laws of nature as blueprints for inventing algorithms. PhyCV features low-dimensionality and high- efficiency, making it ideal for edge computing applications. We demonstrate real-time video processing on NVIDIA Jetson Nano using PhyCV. In addition, these algorithms have the potential to be implemented in real physical devices for fast and efficient computation in the form of analog computing. The open-sourced code is available at https://github.com/JalaliLabUCLA/phycv

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  1. An introduction to Neural Networks for Physicists

    physics.ed-ph 2025-05 conditional novelty 3.0 of 10

    A teaching paper demonstrates perceptron training, PINNs, autoencoders, and SINDy on pendulum examples, with code notebooks provided online.

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