Artificial intelligence is taking notable steps in the field of artificial vision. A key area of development is deep learning, where neural networks are trained on large image data sets to recognize and classify objects, scenes, and events. This has resulted in significant improvements in image recognition and object detection. The integration of computer vision with other technologies is opening several doors to new potentials and scopes for AI.
In the latest innovation, Jalali-Lab @ UCLA has developed a new Python library called PhyCV, which is the first ever physics-based machine vision Python library. This unique library uses algorithms based on the laws and equations of physics to analyze pictorial data. These algorithms mimic how light passes through various physical materials and are based on mathematical equations rather than a set of hand-drawn rules. PhyCV algorithms are based on the principles of a fast data acquisition method called photonic time stretching.
The three algorithms included in PhyCV are: the Phase-Stretch Transform (PST) algorithm, the Phase-Stretch Adaptive Gradient-Field Extractor (PAGE) algorithm, and the Vision Enhancement Through Virtual Diffraction and Coherent Detection (VEViD) algorithm. .
Phase stretch transform (PST) algorithm
The PhyCV library’s PST algorithm identifies edges and textures in images. The algorithm simulates how light travels through a device with particular diffractive properties and then coherently detects the afterimage. The algorithm works best for visually impaired images and has been used in various applications, including improving the resolution of MRI images, identifying blood vessels in retinal images, etc.
Phase-stretching adaptive gradient field extractor (PAGE) algorithm
The PAGE algorithm identifies edges and orientations in images using the principles of physics. Essentially, PAGE mimics the process of light passing through a device with a specific diffractive structure, rendering the image a complex feature. Information about the edges is stored in the real and imaginary components of the result. The researchers mention how PAGE can be used as a preprocessing method in different machine learning problems.
Vision enhancement through Virtual Diffraction and Coherent Detection (VEViD) algorithm
The VEViD algorithm improvises low-light and color images by considering them as a spatially varying field of light and using physical processes such as diffraction and coherent detection. It does this with minimal latency and can therefore increase the accuracy of a computer vision model in low-light circumstances. A particular approximation of VEViD, known as VEViD-lite, can upscale 4K video to up to 200 frames per second. The research team compared the VEViD algorithm with popular neural network models and showed how VEViD displays exceptional image quality with only one to two orders of magnitude higher processing speed.
PhyCV is available at GitHub and can be easily installed via pip. The algorithms in PhyCV can even be applied to real physical devices for more efficient computation. PhyCV certainly looks interesting and as a significant development in the field of machine vision. Consequently, advancements in AI and computer vision are definitely driving a wide range of advanced applications.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
