SBIR-STTR Award

Real-Time Camera-Independent Image Processing System for Long-Range Tactical Imaging Applications
Award last edited on: 6/29/2016

Sponsored Program
SBIR
Awarding Agency
DOD : Army
Total Award Amount
$1,099,974
Award Phase
2
Solicitation Topic Code
A15-026
Principal Investigator
Diego A Socolinsky

Company Information

Equinox Corporation

708 Third Avenue Sixth Floor
New York, NY 10017
   (212) 421-2999
   N/A
   www.equinoxsensors.com
Location: Multiple
Congr. District: 12
County: New York

Phase I

Contract Number: ----------
Start Date: ----    Completed: ----
Phase I year
2015
Phase I Amount
$100,000
We propose to develop a digital image processing system for correction of artifacts due to atmospheric turbulence in long-range video. The system will process video in real time and will not require special optics or cameras. In order to achieve the necessary processing throughput, the system will be designed for implementation on an FPGA-based platform. We will evaluate the correction algorithm on data sets consisting of real imagery acquired through atmospheric turbulence and simulated imagery rendered from target and atmospheric models. By comparing the corrected output to the distortion-free simulated imagery, we will be able to quantitatively evaluate algorithmic performance.

Phase II

Contract Number: ----------
Start Date: ----    Completed: ----
Phase II year
2017
Phase II Amount
$999,974
We propose to develop and implement a digital image processing system for correction of artifacts due to atmospheric turbulence in long-range video. The system will process video in real time and will not require special optics or cameras. In order to achieve the necessary processing throughput, the system will be implemented on an FPGA-based platform. We will evaluate the correction algorithm on data sets consisting of real imagery acquired through atmospheric turbulence and simulated imagery rendered from target and atmospheric models. By comparing the corrected output to the distortion-free simulated imagery, we will be able to quantitatively evaluate algorithmic performance.