Despite many state-of-the-art methods have been proposed for infrared (IR) small target detection, target detectability (TD) and background suppressibility (BS) cannot be significantly improved simultaneously, especially in complex situations. This paper proposes a novel IR small target detection method named improved multi-mode nuclear norm joint local weighted entropy contrast (IMNN-LWEC), which represents the IR target detection task as an optimization problem for tensor decomposition of three components in spatial-temporal domain, including background tensor, target tensor and sparse structure tensor. First, to utilize the spatial and temporal information in an IR sequence effectively, we transform the original IR sequence into a nonoverlapping spatial-temporal patch-tensor. Second, a nonconvex approximation of tensor rank called improved multi-mode weighted tensor nuclear norm (IMWTNN) is proposed to estimate background tensor rank, which is of benefit to separate the background component more completely from the original image. Third, based on structure tensor theory, we introduce a new sparse prior map called local weighted entropy contrast (LWEC) via a designed image entropy operator and a new prior information filter, which can further preserve the target and suppress the background simultaneously. Besides, a novel tube-wise sparse regularization term is designed to identify linear sparse structures. And Frobenius norm is used to characterize noise. Finally, to solve the proposed model, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is designed to retrieve the small targets. Comprehensive experiments on five datasets witness the superior TD and BS performance of the proposed method compared with nine state-of-the-art detection methods.

Infrared small target detection is one of the most important parts of infrared search and tracking (IRST) system. Generally, the small and dim target is of low signal-to-noise ratio and buried in the complicated background and heavy noise, which makes it extremely difficult to be detected with low false alarm rates. To solve this problem, we propose a small target detection method based on multiscale local contrast measure. Different from conventional methods, we novelly measure the local contrast from two aspects: local dissimilarity and local brightness difference. First, we present a new dissimilarity measure called the local energy factor (LEF) to describe the dissimilarity between the small targets and their surrounding backgrounds. Second, the feature of the brightness difference between the small targets and the backgrounds is utilized. Afterward, the local contrast is measured by taking both features of the above into account. Finally, an adaptive segmentation method is applied to extract the small targets from the backgrounds. Extensive experiments on real test data set demonstrate that our approach outperforms the state-of-the-art approaches.

Infrared small target detection (IRSTD) faces various challenges such as long distances, weak features, and small scales. While methodologies based on convolutional neural networks (CNNs) have made strides, they are inherently hampered by a bias toward local reduction, limiting their global interpretive power. Conversely, Transformer-based approaches, though capable of capturing long-range dependencies, struggle with computational inefficiencies due to their quadratic complexity. To surmount these challenges, this article presents MLP-Net, a novel multilayer perceptron (MLP) fusion network for IRSTD. The architecture combines the advantages of CNNs and MLPs to capture global semantic information from local features and significantly enhance feature representation. Additionally, we develop a parallel token interaction mixer (PTIM) that processes the token representations with direction-specific interactive information across the height, width, and channel dimensions on MLPs, dynamically reinforcing the ability of long-range dependency modeling. Complementing this, we devise a contextual selection fusion module (CSFM) to gradually aggregate high-level semantics and low-level details from coarse to fine. This module integrates the complementary characteristics of different layers to promote detection accuracy. Finally, comprehensive experiments on the NUAA-SIRST, NUDT-SIRST, and IRSTD-1K benchmarks demonstrate that the proposed MLP-Net delivers promising detection performance, transcending other state-of-the-art alternatives. The relevant codes will be available at https://github.com/Zhishe-Wang/MLP-Net .

The poor quality images with scarce target characteristics and complicated textures make it tough to enhance target detectability and background suppressibility simultaneously in infrared small target detection. Representation learning has gradually shown superiority, but two key issues arise. The first is constructing an appropriate dictionary. The second is a comprehensive and accurate representation learning model. This paper introduces a low-rank and sparse spatial-temporal tensor representation learning model based on local binary contrast measure (STRL-LBCM), which transforms the target detection into an optimization task for representation and decomposition of background, target and noise components from spatial-temporal aspects. First, we construct an IR spatial-temporal tensor from the original sequence, followed by improving GoDec plus to obtain a sparse part and a background dictionary, which lays the foundation for representation learning model. Then, a rank surrogate named fully connected tensor nuclear norm (FCTNN) is proposed for low-rank coefficient, aiming at low-rank background estimation. Meanwhile, with the sparse part, we design a saliency prior called local binary contrast measure (LBCM), which extracts more effective prior information for guiding target detection. Furthermore, not only Frobenius norm but also a 3D weight total variation (3DWTV) regularization are utilized to preserve background details and remove noise. Finally, an efficient solving framework is designed for the proposed model. Comprehensive experiments reveal that STRL-LBCM provides superior TD, BS and overall performance compared with sixteen target detection methods.

Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.

The infrared small target detection (IRSTD) task presents significant challenges due to low signal-to-clutter ratio, complicated background, and strong interferences. While tensor theory has shown promise in detection performance, three issues regarding damaged tensor construction, inaccurate tensor models, and high computation complexity remain. This study addresses these issues by introducing an independent spatial-temporal perspective, and proposes a fast and separable spatial-temporal tensor completion model. A new tensor structure named separable spatial-temporal patch-tensor pair (SSPP) is conceived to alleviate the dilemma of maintaining neighborhood structure and temporal consistency when constructing image tensors. By treating spatial and temporal dimensions as independent, SSPP enables flexible distribution hypotheses and representations in each dimension. Two tensor models are devised: the spatial model focuses on target enhancement in the spatial dimension, while the temporal one concentrates on suppressing strong interference in the temporal dimension. A long-term memory regularization is further introduced to the temporal model for target movement perception, enhancing its robustness to interferences. By combining these tensor models and employing a coarse-to-fine detection strategy, our method offers an effective solution for IRSTD. Extensive experiments on practical datasets have demonstrated the superiority of the proposed method in terms of target enhancement, background suppression, and detection efficiency.