Infrared (IR) images derived from cloudy skies are always too spatially varied for tiny targets to be detected, especially for single-frame detection. Using the neural networks (NN) nonlinear regression, discrimination, and self-leaning capability, an NN-based method is proposed for tiny point target detection in single-frame IR images with high background clutter. First, the background was estimated by an improved NN-based morphologic filter, the structure element of which was optimized by a two-layer NN. Second, noise characteristics were well studied, and thus a two-level segmentation is presented to delete noises as well as to further remove remaining background components. Last, images with several potential targets were fed to a BP NN that predicted the identity of the input, which was either a target or a pseudo-target. It is these two neural networks that separate target from background and pseudo-target, respectively, with different training destinations, thus avoiding the over-training problem. Results on real data indicate that, given the false alarm probability, the detection probability by this method reaches 98.3percent, which is improved by 11.02percent compared to the traditional approach with fixed SE and without trained NN.
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