PhyCV: The First Physics-inspired Computer Vision Library (2301.12531v2)

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

• The paper presents PhyCV, a novel library that uses physics-based algorithms like PST, PAGE, and VEViD for dynamic image processing.
• It demonstrates efficient edge detection and low-light enhancement, leveraging GPU acceleration on platforms such as the NVIDIA Jetson Nano.
• The work combines theoretical innovation with practical application, paving the way for integration with deep learning and analog computing solutions.

PhyCV: The First Physics-Inspired Computer Vision Library

The paper presents PhyCV, a computer vision library utilizing algorithms derived from physical equations, specifically those governing optical phenomena. This library diverges from traditional sequence-based and deep learning approaches by leveraging the deterministic nature and efficiency of physics-based methods. The algorithms introduced in PhyCV draw inspiration from photonic time stretch, a technique for ultrafast data acquisition exploiting optical pulse dispersion.

Key Algorithms

PhyCV includes three main algorithms: Phase-Stretch Transform (PST), Phase-Stretch Adaptive Gradient-Field Extractor (PAGE), and Vision Enhancement via Virtual diffraction and coherent Detection (VEViD). These algorithms leverage the Nonlinear Schrödinger Equation (NLSE) to model the propagation of light through a medium, treating images as optical entities undergoing dispersive transformations.

1. Phase-Stretch Transform (PST): PST is designed for efficient edge and texture detection, excelling in visually impaired images. It uses a phase-dependent frequency domain transformation to extract high-frequency image features, indicative of edges and textures. This algorithm has been successfully applied in various domains, such as MRI resolution enhancement and retinal vessel detection.
2. Phase-Stretch Adaptive Gradient-Field Extractor (PAGE): PAGE emulates birefringent diffractive propagation to extract edge features across multiple scales and orientations. This approach allows for the characterization of edges with varying spatial frequencies, making it versatile for diverse imaging contexts.
3. Vision Enhancement via Virtual Diffraction and Coherent Detection (VEViD): VEViD reinterprets images as light fields and employs virtual diffraction techniques for low-light and color enhancement. It can significantly improve images captured in challenging lighting conditions and serves as an effective pre-processing step for object detection.

Implications for Edge Computing

PhyCV's low-dimensional and high-efficiency properties make it highly suitable for edge computing applications. The paper demonstrates the real-time processing capabilities of PhyCV on an NVIDIA Jetson Nano, achieving competitive frame rates for edge detection and low-light enhancement. This indicates its potential utility in resource-constrained environments, where computational efficiency is critical.

Performance and Scalability

The implementation of PhyCV supports GPU acceleration, substantially reducing computation times as compared to CPU processing. Performance benchmarks indicate significant improvements in processing times, particularly at high resolutions such as 4K video. This acceleration is particularly beneficial for real-time applications, enhancing the feasibility of deploying PhyCV in practical scenarios.

Theoretical and Practical Contributions

Theoretically, PhyCV introduces a novel paradigm by adapting physical equations into computational algorithms, thereby expanding the methodological toolkit available to computer vision researchers. Practically, its open-source nature encourages community refinement and application across various imaging tasks.

Future Directions

While PhyCV offers an innovative approach, further research could explore integrating these physics-based algorithms with existing deep learning frameworks to amalgamate interpretability with data-driven adaptability. Advancements in analog computing could also synergize with PhyCV's efficient algorithmic structure, paving the way for fast, low-power vision systems.

In summary, PhyCV exemplifies the potential of physics-inspired computational methods in advancing computer vision capabilities, providing a foundation for both theoretical exploration and practical application in edge computing and beyond.