Super-resolution reconstruction and local area processing

Gerald C. Holst; Eugene Cloud; Harry Lee; Teresa Pace; Drew Manville; James Puritz

doi:10.1117/12.724482

4 May 2007 Super-resolution reconstruction and local area processing

Gerald C. Holst, Eugene Cloud, Harry Lee, Teresa Pace, Drew Manville, James Puritz

Proceedings Volume 6543, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVIII; 65430E (2007) https://doi.org/10.1117/12.724482
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Super resolution reconstruction (SRR) improves resolution by increasing the effective sampling frequency. Target acquisition range increases but the amount of increase depends upon the relationship between the optical blur diameter and the detector size. Range improvement of up to 52% is possible. Modern systems digitize the scene into 12 or more bits but the display typically presents only 8 bits. Gray scale compression forces scene detail to fall into a gray level and thereby "disappear." Local area processing (LAP) readjusts the gray scale so that scene detail becomes discernible. Without LAP the target signature is small compared to the global scene dynamic range and this results in poor range performance. With LAP, the target contrast is large compared to the local background. The combination of SRR and LAP significantly increases range performance.

Citation Download Citation

Gerald C. Holst, Eugene Cloud, Harry Lee, Teresa Pace, Drew Manville, and James Puritz "Super-resolution reconstruction and local area processing", Proc. SPIE 6543, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVIII, 65430E (4 May 2007); https://doi.org/10.1117/12.724482

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KEYWORDS

Sensors

Contrast transfer function

Super resolution

Eye

Modulation transfer functions

NVThermIP

Target acquisition

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