Infrared Patch-Image Model for Small Target Detection in a Single Image

TLDR

Robust detection of small targets is a key technique in infrared search and tracking applications. The paper proposes a novel small target detection method for a single infrared image. The method generalizes the infrared image model to a patch‑image model, formulates target detection as a low‑rank/sparse matrix recovery problem solved by stable principal component pursuit, and refines results with adaptive segmentation and post‑processing. Experiments on synthetic and real data demonstrate that the proposed method is more stable across target sizes and signal‑to‑clutter ratios and outperforms conventional baseline methods.

Abstract

The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.