Single-Frame Infrared Small Target Detection by High Local Variance, Low-Rank and Sparse Decomposition

Single-Frame Infrared Small Target Detection (SF-IRSTD) has grown in popularity due to its broad application. Several models based on Low-Rank and Sparse Decomposition (LRSD) have been proposed recently and have shown excellent performance. Nevertheless, these methods regard the non-low-rank sparse points as the targets, obscuring the distinction between the non-low-rank noise and the target in the infrared image. To address this issue, we consider that the targets usually have a high local salience compared to the noise and propose a novel method using High Local Variance, Low-Rank and Sparse Decomposition (HiLV-LRSD), identifying the sparse points with high local salience and non-low-rank as the targets and the remaining regions as the background. Specifically, we first use the local variance to represent local salience and propose an LV* norm to constrain the background’s low-rank and local variance. Then, we define an adaptively re-weighted L1 (Llv,1) norm to constrain the sparsity of the target and enhance the influence of local variance. Finally, we propose an optimization framework and solve it by a Partially Iterative Alternating Direction Method of Multipliers (PI-ADMM). We evaluate our proposed method on the publicly available dataset SIRST and compare it to 10 state-of-the-art SF-IRSTD methods. The results show that our proposed method outperforms these methods.