Wide Field of View Imaging Using Optical Multiplexing

In a multiplexed image, multiple fields-of-view (FoVs) are superimposed onto a common focal plane. The attendant gain in sensor FoV provides a new degree of freedom in the design of an imaging system, allowing for performance tradeoffs not available in traditional optical designs. We describe a class of optical systems that can achieve high density image multiplexing through a novel division of aperture technique. Fundamental design considerations and performance attributes for this sensor architecture are discussed. Results from a prototype five-channel sensor are presented in a sparse-scene star tracking demonstration. A six-channel optically multiplexed prototype sensor is used to reconstruct imagery from information rich dense scenes through dynamic image shifting.

This material is based upon work supported by the Office of the Secretary of Defense under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Office of the Secretary of Defense.

Dr. Yaron Rachlin

Senior Staff, MIT Lincoln Laboratory on September 13, 2019 at 11:45 AM in EB2 1230

Yaron Rachlin holds a Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University and is a Senior Staff in the Advanced Capabilities and Technologies Group at MIT Lincoln Laboratory. He has over 40 publications and patents in diverse areas including information theory, computational imaging, optical interferometric imaging, and compressed sensing. His work has been recognized with an award for Innovative Imaging from the Space Systems and Technology Division at Lincoln Laboratory and he received Lincoln Laboratory’s Best Invention Award. He has led multiple projects that demonstrated novel computational imaging systems for wide field of view infrared imaging, satellite-based Earth observation, and spectral imaging. He is currently leading a research team to develop methods for efficiently detecting rare and faint signals across a variety of sensor modalities and data types.

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