Multiscale facet model for infrared small target detection

In this paper, we proposed a new robust infrared small target detector that is more suitable for complex background with low signal-to-clutter ratio. The original image is decomposed into sub-bands in different orientations by using the directional second-order directional derivative (DSODD) filters deduced from the facet model. The multiscale facet model (MFM) analysis is developed by using a series of multiscale DSODD filters, which are obtained by filling zeros in the basic DSODD filter. Based on MFM, an MFM matrix is constructed, and the normalized determinant of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed to enhance the target signal and suppressing the background clutter. The experimental results on a set of real infrared images demonstrate that the proposed approach is effective and is superior to the traditional small target detection methods in terms of the pertained quantitative detection evaluation indexes, such as the signal-to-clutter ratio gain and the background suppression factor.