Signal conditioning algorithms for enhanced tactical sensor imagery

Abstract

In tactical sensor imagery there always is a need for less noise, higher dynamic range and more resolution. Although recent developments lead to better and better Focal Plane Array (FPA) camera systems, modern infrared FPA camera system are still hindered by non-uniformities, a limited signal-to-noise ratio and a limited spatial resolution. The current availability of fast and inexpensive digital electronics allows the use of advanced real-time signal processing to address the need for better image quality. We will present results of signal-conditioning algorithms, which achieve significant better performance with regard to the FPA problems given above. Scene-Based Non-Uniformity Correction (SBNUC) can provide an on-line correction of existing and evolving fixed-pattern noise. Dynamic Super Resolution (DSR) improves the signal-to-noise ratio, while simultaneously improving spatial resolution. The signal-conditioning algorithms can handle camera movements, high temporal noise levels, high fixed-pattern noise levels and large moving objects. The Local Adaptive Contrast Enhancement (LACE) algorithm does effectively compress the 10, 12 or 14 bits dynamic range of the corrected imagery towards a 6 to 8 bits dynamic range for the display system, without the loss of image details. In this process, it aims at keeping all information in the original image visible. We will show that the SBNUC, DSR, mosaic generation, and LACE can be integrated in a very natural way resulting in excellent all-round performance of the signal-conditioning suite. We will demonstrate the application of SBNUC, DSR, Mosaicking and LACE for various imaging systems, showing significant improvement of the image quality for several imaging conditions.