PhyCV: The First Physics-inspired Computer Vision Library

TL;DR

PhyCV introduces a novel computer vision library based on physics-inspired algorithms that emulate light propagation, offering high efficiency and suitability for edge computing and real-time applications.

Contribution

It is the first library to utilize physics-derived algorithms for computer vision, enabling low-dimensional, efficient processing suitable for edge devices.

Findings

Real-time video processing demonstrated on NVIDIA Jetson Nano

Physics-inspired algorithms outperform traditional methods in efficiency

Potential for implementation in physical analog computing devices

Abstract

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