Infrared small target detection is an important research area of computer vision and often a key technique in Infrared
Search and Track (IRST) systems. Many algorithms have been reported for this purpose. The facet-based method is one
of novel algorithms and is shown as robust and efficient, but it does not perform well in target preservation. The method
cannot detect peripheral pixel of target, which causes information loss of target intensity distribution and affects post
processing of detection, such as target tracking and recognition. In this paper an improved algorithm is developed for
solving this shortcoming. The detection behavior of the facet model is further analyzed. Small target is surrounded by
background, so local image edge that indicates target contour can be represented by zero-crossings of the second partial
derivatives. The improved algorithm uses facet model to fit local intensity surface and detect potential targets using
extremum theory, then the zero-crossings of the second partial derivatives of the fitting function in each potential target's
neighborhood are found and the pixels inside the zero-crossing contour are restored to the potential target. In
experiments involving typical infrared images target intensity distribution information is well preserved by proposed
algorithm and its execution time is also acceptable.
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