Target-acquisition performance in undersampled infrared imagers: static imagery to motion video

Area of Science:

• Imaging science
• Human visual perception
• Signal processing

Background:

• Undersampled imagers often suffer from aliasing and reduced resolution.
• Motion can introduce artifacts but may also offer opportunities for image enhancement.
• Super-resolution techniques aim to improve image detail beyond the sensor's native capabilities.

Purpose of the Study:

• To investigate how sensor or target motion affects target-acquisition performance in undersampled imagers.
• To quantify the improvement in observer performance as a function of target motion rate.
• To understand the role of the human visual system in motion-based super-resolution.

Main Methods:

• An experiment was conducted using a midwave thermal imager mounted on a two-axis gimbal.
• Sensor motion rates were varied from 0.25 to 1 instantaneous field of view per frame.
• Short integration times were used to minimize motion blur.

Main Results:

• Target-acquisition performance improved with both sensor and target motion.
• The human visual system demonstrated a super-resolution reconstruction capability, mitigating aliasing.
• Effective resolution was higher with motion compared to static imagery, largely independent of motion velocity.

Conclusions:

• Motion enhances target acquisition in undersampled imaging systems.
• The human visual system inherently performs motion-based super-resolution.
• Current super-resolution reconstruction techniques may have limited additional benefits in systems with inherent motion.