The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems.

Infrared small target detection technology has received extensive attention due to its advantages in long-distance monitoring. However, there is much room for improvement in its performance due to complex backgrounds and the lack of distinct features in small targets. Many specific scenarios can lead to target loss, such as edge-adjacent targets, intersecting targets, low contrast caused by locally bright backgrounds, and false alarms induced by globally bright backgrounds. To address these issues, we have identified the positional correlation differences between the local background location and whether the target can be perceived by the human eye, thereby introducing geographic information weights to represent this correlation difference. We first constructed a non-concentric Gaussian difference structure to prevent the central target energy loss caused by traditional concentric filters. Based on this, we introduced Gabor filters, which have the capability of directional feature extraction and position correlation representation, into the non-concentric differential structure. By adjusting the relative position of the Gabor filter center and configuring frequency parameters based on geographic information, we optimized the filter weights to handle complex situations, such as targets being close to background clutter or other targets. Subsequently, an improved logarithmic function was applied to adjust the overall saliency of candidate targets, preventing the loss of low-contrast targets and the residual high-energy background clutter. Extensive experiments show that our method exhibits effective detection performance and robustness in four application scenes and three challenging image distribution scenes.