# Time Stretch Inspired Computational Imaging

## TL;DR

This paper introduces a novel class of physics-inspired algorithms for feature extraction in digital imaging, leveraging dispersive light propagation and phase detection to achieve superior dynamic range and potential energy efficiency.

## Contribution

It presents a new computational imaging approach inspired by time stretch physics, offering improved feature extraction capabilities over traditional methods.

## Key findings

- Algorithms demonstrate superior dynamic range.
- Potential for energy-efficient image processing.
- Scalable to practical applications.

## Abstract

We show that dispersive propagation of light followed by phase detection has properties that can be exploited for extracting features from the waveforms. This discovery is spearheading development of a new class of physics-inspired algorithms for feature extraction from digital images with unique properties and superior dynamic range compared to conventional algorithms. In certain cases, these algorithms have the potential to be an energy efficient and scalable substitute to synthetically fashioned computational techniques in practice today.