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Computational Imaging Lunch: 3D Compressed Sensing with a Diffuser

Title: 3D Compressed Sensing with a Diffuser 
Speakers: Grace Kuo, Nick Antipa

Description: When capturing higher dimensional data (3D or light-fields) with a 2D sensor, one usually must sacrifice either spatial resolution or time resolution. For example, a plenoptic light-field camera distributes pixels over four dimensions resulting in lower spatial resolution than a traditional camera. Scanning and multi-shot methods can maintain high spatial resolution, but they sacrifice time resolution. In this work, we propose an alternative: If we can encode 3D information on the 2D sensor in such a way that it meets the requirements of compressed sensing, than we should be able to recover the 3D object from a single images without loss of resolution, assuming the object is sparse in some domain.  In this talk, we will give an overview of compressed sensing, demonstrate that a diffuser meets the practical requirements of compressed sensing, and show results from our prototype system.

This paper by Candes gives a nice introduction to compressed sensing:

Nick Antipa’s paper from ICCP last year discusses how a diffuser can be user to capture light-fields: