NASA SBIR 2018-I Solicitation

Proposal Summary

 18-1- S5.03-1266
 Machine Learning and Deep Learning for Science and Engineering
 Advancing Hyperspectral Imaging through Integrated Compressive Sensing / Inpainting via Machine Learning
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Sivananthan Laboratories, Inc.
590 Territorial Drive, Unit H
Bolingbrook , IL 60440-4887
(630) 226-0080

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Christopher Buurma
590 Territorial Drive, Unit H Bolingbrook, IL 60440 - 4887
(630) 226-0080

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)
Paul Boieriu
590 Territorial Drive, Unit H Bolingbrook, IL 60440 - 4887
(630) 226-0080
Estimated Technology Readiness Level (TRL) :
Begin: 2
End: 3
Technical Abstract

We propose to use advanced algorithms for compressive sensing (CS) using inpainting which utilize machine learning to improve the capability of existing or novel hyperspectral imaging (HSI) systems. These approaches will allow for an order of magnitude improvement in the frame acquisition speed of existing detectors at a high compression ratio with no loss of resolution, while at the same time permitting them to overcome object obscuration and radiation damage phenomena in excess of existing technologies in this area. To accomplish these goals we propose to develop new hyperspectral sensors that intentionally omit pixels that will be recovered using model based compressive sensing.  This provides the ability to selectively acquire a small number of pixels in an existing imaging array to form the complete image with no loss of image resolution or fidelity (or the ability to select an even smaller number with an acceptable loss of resolution). In addition, the algorithms that will be developed can adapt to the form of key image types using machine learning, so that the number of pixels can be reduced further, or that the pixels used for the image can be adaptively changed to avoid obscuration/damaged pixels. This type of imaging system has significant benefits for imaging/detection across a whole range of NASA programs and missions for recovery of damaged equipment or scenes, image enhancement, data compression or using novel designs with reduced pixel densities.  This imaging approach will also automatically compress all images obtained by the level of sub-sampling used to form the image, reducing transmission and storage costs significantly, even without using additional lossless data compression before transmission.

Potential NASA Applications

This technology can be applied to both existing hyperspectral imaging systems and those being developed. The approach can be applied to both division of time and division of space systems, maintaining resolution while reducing capture time and noise. Additional applications are available in robust data compression for storage and transmission over long distances. This approach also provides fault tolerance, system recovery and image enhancement for fielded imagers with lost pixels.

Potential Non-NASA Applications

Commercialization will come in two forms: enhancement of existing technologies which can leverage CS, and through selling custom-made imaging systems as build in functionality. Hyperspectral imaging systems are used in many applications including chemical and biological detection, manufacturing, environmental surveys of CO2, pollution, and leakages. Also the improvement of existing infrared imaging systems by tolerating higher bad pixel densities reducing cost and improving performance.

Form Generated on 05/25/2018 11:45:36