PhyCV: a physics-inspired computer vision Python library from UCLA

Jalali-Lab@UCLA has released PhyCV, the first physics-inspired computer vision library.

Unlike traditional algorithms that consist of a sequence of hand-crafted empirical rules, PhyCV algorithms are based on the physical laws of nature, such as the equations of light propagation through a physical medium.

Color enhancement using VEViD in PhyCV

PhyCV contains three main algorithms:

  • Phase-Stretch Transform (PST)
  • Phase-Stretch Adaptive Gradient-Field Extractor (PAGE)
  • Vision Enhancement via Virtual diffraction and coherent Detection (VEViD)

All algorithms have CPU and GPU versions.

The YOLO-v3 object detector is improved by VEViD in PhyCV

The PhyCV algorithm’s modular form closely mimics the physical process from which it was derived. The new released library needs to be improved in the future, due to its two main limitations:

1. Real-time video processing’s bottleneck for I/O (Input/Output)

In order to use the GPU-accelerated PhyCV algorithms, the frames must first be collected and buffered in the CPU before being transferred to the GPU. This procedure takes a long time and creates a bottleneck for real-time algorithms.

2. Lack of image-specific parameter adaptivity

PhyCV algorithms’ parameters now need to be manually adjusted for various images. The library has implemented a set of pre-selected parameters and it performs very well for a variety of photos. The lack of parameter adaptivity is still a limitation of PhyCV and needs to be addressed in a future research.

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