Infrared small target detection (IRSTD) task is vital in practical applications. It is still a challenge when the target size is very small and the local signal-to-noise ratio is particularly low. This paper proposed an Infrared Tall Patch-Matrix (ITPM) model, which takes a novel perspective to construct a lower-rank patch matrix structure to improve the detection performance of low-contrast small targets. Specifically, we use a sliding split window to reconstruct the original image into a suitable low-rank structure called Tall Patch-Matrix, which can increase the detection rate of low-contrast small targets and suppress most noise. Second, the High Local Variance Low-Rank and Sparse Decomposition (ITPM-HiLV-LRSD) method is used to perform low-rank and sparse decomposition of the Infrared Tall Patch-Matrix, and a Thin Singular Value Decomposition (Thin SVD) optimization strategy is proposed to further reduce the computational complexity. Given the absence of open literature datasets for detecting infrared targets in low-contrast small scenarios, we created a Low-contrast Small Target Detection Dataset (LSTDD) comprising 600 infrared target detection images with varied sky backgrounds. This dataset simulates low-contrast small targets across different signal-to-noise ratios. To demonstrate the generalizability of our method, we also conducted experiments on a representative low-contrast subset of real-world images from the SIRST dataset. Compared with six state-of-the-art methods, our proposed method excels in detecting low-contrast small targets with superior performance.
Liu et al. (2026) studied this question.