Computational imaging

The concept and goal of computational imaging is to leverage computational schemes and power to improve the capabilities of conventional cameras/sensors.

Compressive video

Due to the per-pixel “overhead” time consumption, ┬ámodern sensors face a fundamental space-time resolution trade-off. The more pixels to sample, the more time to take. This issue becomes significant if high performance (high spatial and temporal resolution) video sensing is the goal. Compressive video is one of the research topics to overcome this issue. The basic assumption is that data is redundant and “compressible”. Thus, it is possible to sample video data at a much lower ratio while maintaining a reasonable reconstruction fidelity.

  • compressive holographic video

This project seeks to answer the question: how do we encode a 4-dimensional (3D position and time) scene into a single image and how to recover it?

  • lens-free on-chip video

One of the applications of compressive video is to monitor in vivo biological samples. This task requires an imaging platform with large field of view and high resolution. The lens-free on-chip setup stands out due to its compactness and cost-effectiveness. In biomedical imaging, people are more interested in phase information of the object as it potentially reveals more information. Thus and furthermore, how do we recover phase when a compressive sampling scheme is applied?