Image enhancement for automatic target detection

Typically when one tackles an Automatic Target Detection (ATD) problem in image data, an assumption is made that the sample of target pixels is statistically different from the sample of background pixels in an immediate neighborhood of the target. Algorithms are then devised to recognize groups (or individual) outlier pixels as indicating a possible target to be further processed by an Automatic Target Recognition (ATR) algorithm. In this paper, we present a novel approach for image enhancement that raises the intensity of outlier pixels while suppressing background pixels. Thus, simple thresholding of the enhanced image becomes a powerful ATD algorithm. The approach is not a pixel-level algorithm as it is derived and implemented in the frequency domain. This also implies that, since the algorithm is not specifically intensity-based, low SNR targets can be significantly enhanced if the target frequency domain characteristics are outliers compared to the background frequency domain characteristics. Full performance statistics over a large and clutter rich IR dataset will be presented and compared to other ATD algorithms.