Infrared small target detection using sparse representation

Sparse representation has recently been proved to be a powerful tool in image processing and object recognition. This paper proposes a novel small target detection algorithm based on this technique. By modelling a small target as a linear combination of certain target samples and then solving a sparse l(0)-minimization problem, the proposed apporach successfully improves and optimizes the small target representation with innovation. Furthermore, the sparsity concentration index (SCI) is creatively employed to evaluate the coefficients of each block representation and simpfy target identification. In the detection frame, target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model (GIM), and then sparse model solvers are applied to finding sparse representation for each sub-image block. Finally, SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position. The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.