We describe a new computational approach to image analytics and its application to feature enhancement. The algorithm reveals latent features in the image by a transformation known as the Phase Stretch Transform. This computationally efficient transform emulates the propagation of light through a physical medium followed by detection of light’s complex amplitude. We show that the phase of the transform reveals transitions in image intensity and can be used for edge detection with excellent low light level sensitivity. When the diffractive medium has a warped frequency response, the transform engineers the space-bandwidth product of the image with potential application in data compression. Image processing inspired by optical physics has emerged from the research on Photonic Time Stretch, a time-domain signal processing technique that employs temporal dispersion to slow down, capture, and digitally process fast waveforms in real time. This talk will focus on the Phase Stretch Transform (PST), its extension to machine learning and applications in radiology, astronomy and security image analytics.
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