Time Stretch Inspired Computational Imaging
This addresses the problem of improving feature extraction in computational imaging for applications requiring better dynamic range, though it appears incremental as it builds on known physics principles.
The paper tackles feature extraction from digital images by exploiting dispersive light propagation and phase detection, resulting in algorithms with superior dynamic range compared to conventional methods.
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.