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.

The development of effective detection algorithms under a complex background for small infrared targets has always been difficult. The existing algorithms have poor resistance to complex backgrounds, easily leading to false alarms. Furthermore, each target and its background correspond to different component signals, and changes in the components in space cause observation uncertainty. Inspired by this phenomenon, we propose a method for detecting small targets in complex backgrounds using local uncertainty measurements based on the compositional consistency principle. First, a multi-layer nested sliding window is constructed, and a local component uncertainty measure algorithm is used to suppress the complex background by evaluating the component comprising local area signals. Subsequently, an energy weighting factor is introduced to reinforce the energy information embedded in the target in the uncertainty distribution map, thereby enhancing the target signal. Validation results obtained on real infrared images show that the energy-weighted local uncertainty measure performs better when detecting small targets hidden in complex backgrounds, with a high signal-to-clutter ratio gain and background suppression factor.

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.

The development of intelligent control and visual navigation, along with the ongoing increase in imaging distance, has led to realistic demands for weak and small target detection. However, figuring out how to reduce the interference of various complex factors while achieving a high detection rate and low false alarm rate for real-time infrared small target detection is still a significant challenge. We propose a computationally simple single-frame infrared small target fast detection method applicable to complex scenes. First, a novel anisotropy filter bank suitable for small target feature extraction is constructed and modified with a point spread function. This makes it possible to map the salient features and spatial features of the target in the spatial domain using the imaging mechanism. We then combined two sets of feature parameters using the filtered features, including isotropy measure and directional energy factors, to enable the detection of small targets. Experimental findings showed that the algorithm performs well in scenarios involving complicated clouds, urban ground, and vegetation. The software also achieved excellent real-time performance because of its straightforward computational design.

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.