# PhyCV: The First Physics-inspired Computer Vision Library

arXiv: https://arxiv.org/abs/2301.12531 · HTML: https://mlsanity.com/paper/2301.12531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 29 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [VEViD: Vision Enhancement via Virtual diffraction and coherent Detection](https://mlsanity.com/paper/2208.12366/) (2022)
- [Feature Enhancement in Visually Impaired Images](https://mlsanity.com/paper/1706.04671/) (2017)

## Built on by (most cited first, 1 of 1)

- [Standardizing Medical Images at Scale for AI](https://mlsanity.com/paper/2603.15980/) (2026)

## Abstract (arXiv)

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