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 nonlow-rank sparse points as the targets, obscuring the distinction between the nonlow-rank noise and the target in the infrared image. To address this issue, we consider that the targets usually have a high local salience (HLS) compared to the noise and propose a novel method using high local variance, LRSD (HiLV-LRSD), identifying the sparse points with HLS and nonlow 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 reweighted L1 ( $L_{lv,1}$ ) 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 ten state-of-the-art SF-IRSTD methods. The results show that our proposed method outperforms these methods.