Infrared imaging plays an important role in space-based early warning and anti-missile guidance due to its particular imaging mechanism. However, the signal-to-noise ratio of the infrared image is usually low and the target is moving, which makes most of the existing methods perform inferiorly, especially in very complex scenes. To solve these difficulties, this paper proposes a novel multi-frame spatial–temporal patch-tensor (MFSTPT) model for infrared dim and small target detection from complex scenes. First, the method of simultaneous sampling in spatial and temporal domains is adopted to make full use of the information between multi-frame images, establishing an image-patch tensor model that makes the complex background more in line with the low-rank assumption. Secondly, we propose utilizing the Laplace method to approximate the rank of the tensor, which is more accurate. Third, to suppress strong interference and sparse noise, a prior weighted saliency map is established through a weighted local structure tensor, and different weights are assigned to the target and background. Using an alternating direction method of multipliers (ADMM) to solve the model, we can accurately separate the background and target components and acquire the detection results. Through qualitative and quantitative analysis, experimental results of multiple real sequences verify the rationality and effectiveness of the proposed algorithm.

In the combat system, infrared target detection is an important issue worthy of study. However, due to the small size of the target in the infrared image, the low signal-to-noise ratio of the image and the uncertainty of motion, how to detect the target accurately and quickly is still difficult. Therefore, in this paper, an infrared method of detecting small moving targets based on a coarse-to-fine structure (MCFS) is proposed. The algorithm mainly consists of three modules. The potential target extraction module first smoothes the image through a Laplacian filter and extracts the prior weight of the image by the proposed weighted harmonic method to enhance the target and suppress the background. Then, the local variance feature map and local contrast feature map of the image are calculated through a multiscale three-layer window to obtain the potential target region. Next, a new robust region intensity level (RRIL) algorithm is proposed in the spatial-domain weighting module. Finally, the temporal-domain weighting module is established to enhance the target positions by analyzing the kurtosis features of temporal signals. Experiments are conducted on real infrared datasets. Through scientific analysis, the proposed method can successfully detect the target, at the same time, the ability to suppress the background and the ability to improve the target has reached the maximum, which verifies the effectiveness of the algorithm.