Scene-based nonuniformity correction using texture-based adaptive filtering

Abstract

The detectors within an infrared focal plane array (FPA) characteristically have responses that vary from detector to detector. It is desirable to remove this "nonuniformity" for improved image quality. Factory calibration is not sufficient since nonuniformity tends to drift over time. Field calibration can be performed using uniform temperature sources but requires briefly obscuring the field-of-view and leads to additional system size and cost. Alternative "scene-based" approaches are able to utilize the normal scene data when performing non-uniformity correction (NUC) and therefore do not require the field-of-view to be obscured. These function well under proper conditions but at times can introduce image artifacts such as "ghosting". Ghosting results when scene conditions are not optimal for NUC. The scene-based approach presented in this paper estimates a correction term for each detector using spatial information. In parallel, motion estimation and texture features are used to identify frames and regions within frames that are suitable for NUC. This information is then employed to adaptively converge to the proper correction terms for each detector in the FPA.