| Infrared cameras can directly measure the apparent temperature of objects, providing thermal imaging. However, the raw output from most infrared cameras suffers from a strong, often limiting noise source called nonuniformity. Manufacturing imperfections in infrared focal planes lead to high pixel-to-pixel sensitivity to electronic bias, focal plane temperature, and other effects. The resulting imagery can only provide useful thermal imaging after a nonuniformity calibration has been performed. Traditionally, these calibrations are performed by momentarily blocking the field of view with a at temperature plate or blackbody cavity. However because the pattern is a coupling of manufactured sensitivities with operational variations, periodic recalibration is required, sometimes on the order of tens of seconds.;A class of computational methods called Scene-Based Nonuniformity Correction (SBNUC) has been researched for over 20 years where the nonuniformity calibration is estimated in digital processing by analysis of the video stream in the presence of camera motion. The most sophisticated SBNUC methods can completely and robustly eliminate the high-spatial frequency component of nonuniformity with only an initial reference calibration or potentially no physical calibration. I will demonstrate a novel algorithm that advances these SBNUC techniques to support all spatial frequencies of nonuniformity correction.;Long-wave infrared microgrid polarimeters are a class of camera that incorporate a microscale per-pixel wire-grid polarizer directly affixed to each pixel of the focal plane. These cameras have the capability of simultaneously measuring thermal imagery and polarization in a robust integrated package with no moving parts. I will describe the necessary adaptations of my SBNUC method to operate on this class of sensor as well as demonstrate SBNUC performance in LWIR polarimetry video collected on the UA mall. |
