Infrared (IR) small target detection has been widely used in military and civil fields. However, due to the insufficient feature integration capabilities of existing methods, effectively separating strong background clutter and targets in complex scenes remains difficult. To address this issue, we propose a two-stage feature complementary improved tensor low-rank sparse decomposition (TSFC-TLRSD) method. The detection process is divided into two stages: tensor initialization and tensor decomposition, effectively integrating local and non-local features. In the tensor initialization stage, inspired by the local saliency of the target and the local consistency of the background, we design a three-layer directional filtering (TLDF) operator for preliminary clutter suppression and target enhancement. Then, to promote the complementary advantages of local and non-local features, we refer to the TLDF and the original image to provide a targeted initialization strategy for the tensor low-rank sparse decomposition (TLRSD) model. In the tensor decomposition stage, we develop a robust partial sum of the tubal nuclear norm (RPSTNN) as a non-convex approximation of tensor rank, which can adaptively adjust the singular value distribution, thus adapting to diversity scenes. Meanwhile, we finely adjust the balance between low-rank and sparse components in the model-solving process through a nonlinear reweighting strategy, accelerating the optimization convergence speed and improving the model's background recovery ability. Extensive experiments on five practical datasets demonstrate that the proposed method is more effective and robust compared to ten state-of-the-art approaches.

Infrared (IR) small target detection is widely utilized in both military and civilian sectors. Despite the development of numerous advanced methods for detecting small targets, improving overall performance in complex scenes remains a significant challenge. To address this issue, we propose a joint partial tensor correlated total variation and k -means clustering (PTCTV-KMC) method that integrates local and global features. The proposed method comprises two stages: seed point (i.e., candidate target) search and seed point discrimination. In the seed point search stage, a tensor low-rank sparse decomposition model is first used to decompose the IR image into target and background images. To reduce residual noise and background edges in the target image, we designed a partial tensor correlated total variation (PTCTV) norm. This norm effectively constrains the global low-rankness and local smoothness of the background, and enhances the model's focus on image detail information. Subsequently, leveraging the global sparsity of the target, a density peak search technique is employed to locate seed points in the target image. In the seed point discrimination stage, k-means clustering is utilized to improve the accuracy of the local contrast measure (LCM) in scenarios with uncertain target distribution and mixed background components. By calculating the LCM for each seed point, we further suppress background clutter and enhance real targets. Extensive experiments demonstrate that the proposed method exhibits superior overall performance compared to advanced methods and achieves satisfactory computational speed.

Infrared (IR) small target detection is a central technology used in IR detection systems. However, it is challenging to quickly and accurately detect small targets of different sizes and shapes in complex scenes. In this letter, we propose a method based on energy difference and structural difference measurement (EDASDM) for the fast detection of small IR targets. First, based on a center-edge distribution, we categorize the target into central and edge regions to perform the novel three-layer window measurement (NTWM) and extract target candidate pixels. Subsequently, target and background clutter are further distinguished by their energy difference (ED) and structural difference (SD). Second, the small IR targets of different sizes and shapes are extracted using adaptive threshold segmentation. The experimental results showed that our algorithm achieves superior detection and real-time performance to some state-of-the-art algorithms.

Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. In this paper, an uncertainty measurement method based on local component consistency is proposed to suppress the complex background and highlight the detection target. The method analyzes the local signal consistency of the image. It then constructs a confidence assignment function and uses the mutation entropy operator to measure local uncertainty. Then, the target energy information is introduced through an energy-weighting function to further enhance the signal. Finally, the target is extracted using an adaptive threshold segmentation algorithm. The experimental results show that the algorithm can effectively detect small infrared targets in complex backgrounds. And, the algorithm is at the leading edge in terms of performance; the processing frame rate can reach 3051 FPS (frame per second), 96 FPS, and 54 FPS for image data with a resolution of 256 × 256, 1920 × 1080, and 2560 × 1440, respectively.