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 athttps://github.com/Zhishe-Wang/MLP-Net.

Infrared small target detection (IRSTD) remains challenging due to low signal-to-clutter ratios, unpredictable environmental interference, and weak spatial features. Convolutional neural network (CNN)-based methods excel at extracting local details but often fail to capture long-range dependencies, while Transformer-based approaches model global interactions through self-attention mechanisms but suffer from high computational costs. To overcome these challenges, we introduce a novel parallel interactive kolmogorov–arnold network, termed PKNet. In this architecture, the CNN branch is designed to extract fine-grained local features, while the KAN branch leverages learnable univariate functions to model contextual dependencies. To further strengthen global feature representation, we design a multi-grained KAN (MG-KAN) Block, which enhances context modeling by promoting nonlinear interactions across multiple token dimensions, enabling efficient feature extraction while preserving long-range dependencies. Moreover, we develop a cyclic interactive fusion module that facilitates bidirectional information refinement between the CNN and KAN branches. This module dynamically aligns and integrates multi-scale local and global features, significantly improving the network’s ability to distinguish small targets from complex backgrounds. Extensive experiments on three public benchmarks demonstrate that PKNet achieves superior performance in terms of both detection accuracy and efficiency, substantially outperforming existing state-of-the-art methods. The code will be available at:https://github.com/Zhishe-Wang/PKNet.

Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem,i.e., head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted; 2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group-wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals.

Infrared (IR) small target detection has played an essential role in civilian and military fields. Considering the unsatisfactory detection performance of existing methods, this paper proposes an IR small target detection method named OSP-IRTD, including a principal component analysis-derived orthogonal subspace projector for background suppression and target enhancement, and a designed information filter for residual interference removal. Comprehensive analysis on three IR sequences with four competitive methods demonstrates the superior target detectability (TD), background suppressibility (BS) and overall performance of OSP-IRTD.

Infrared (IR) small target detection is a crucial aspect in both military and civilian fields. Due to the poor quality and complex scenarios, the existing technologies lack superior real-time and overall detection performance. Tensor analysis has shown superiority, but there are three key issues. The first pertains to appropriate tensor-structured data, the second concerns a more comprehensive tensor decomposition architecture, and the third is to achieve more satisfactory real-time performance. This article proposes an IR small target detection method named 4-D spatial–temporal tensor decomposition with block term decomposition-based norm and multidirectional derivative-based priors (4DST-BTMD). It converts the target detection task into a low-rank and sparse decomposition (LRSD) optimization problem of decomposing background, target, and noise tensors from 4-D spatial–temporal domain. First, we construct a 4-D spatial–temporal tensor, followed by projecting it into a constructed data-sphered space, which lays the data foundation for decomposition. Then, based on the 4-D sphered tensor, a low-rank surrogate named block term decomposition-based norm (BTDN) for background estimation is proposed, which fully integrates global information across different dimensions. Meanwhile, more effective salient 4-D prior tensors based on multidirectional derivatives are designed to effectively guide the model to focus on significant areas. Finally, an efficient ADMM-based solving framework is designed for the LRSD model. Furthermore, with seven state-of-the-art competitive methods, extensive experiments and analysis illustrate that the proposed 4DST-BTMD not only demonstrates the superiority in background suppressibility (BS) and target detectability (TD) but also has satisfactory real-time 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.

Due to the poor quality image with low signal-to-clutter ratio (SCR), infrared (IR) small target detection is faced with great challenges in the remote sensing field. Despite the fact that the local contrast measure (LCM) has been widely applied for IR target detection, the existing LCM-based methods suffer from weak target detectability (TD) or background suppressibility (BS) in complicate background. In this paper, we propose a novel IR small target detection method based on improved tri-layer window local contrast measure (TrLCM). With an additional isolation circle in TrLCM, the influence of background on target detection is reduced to a certain extent. Besides, background suppressibility is also promoted through a designed adaptive adjustment coefficient. Comprehensive experiments and analysis on three datasets verify that the proposed TrLCM achieves advanced TD, BS and overall performance.

Infrared small target detection (IRSTD) is a challenging task in the remote sensing field, due to the low signal-to-clutter ratio (SCR) and intricate background. Although local contrast measure (LCM) has been widely applied for IRSTD, the existing LCM-based methods suffer from high sensitiveness to heavy clutters. To resolve this problem, this paper proposes a novel LCM named omnidirectional mirror gradient dissimilarity measure (O-MGDM) for small target enhancement. A concept of mirror gradient dissimilarity (MGD) is first presented to evaluate the pixel gradient inconsistency of opposite directions. It is analyzed that small target pixels exhibit considerable MGD in all directions, while heavy clutters hold large MGD in certain directions. Then, the novel O-MGDM is proposed by uniting the omnidirectional MGDs, which achieves target enhancement and heavy clutter suppression simultaneously. After that, an adaptive threshold is used to segment the targets readily. Extensive experimental results on practical data sets demonstrate that the proposed O-MGDM achieves advanced detection performance.

Band selection (BS) is an effective means to solve the problems of spectral redundancy and Hughes phenomenon in hyperspectral images (HSIs). However, existing BS methods fail to take into account the representativeness, redundancy, and information content of the selected bands simultaneously, and most of them lack consideration of the inherent nonlinear relationship between bands. To address these problems, we propose a novel unsupervised BS framework that can comprehensively consider band representativeness, redundancy, and information content (RRI) in this letter. The band representativeness is estimated by a three-dimensional convolutional autoencoder, which can capture the inherent nonlinear relationship between the bands and leverage the spatial information of the HSI. The redundancy and the information content of a band subset are restricted and enhanced by the correlation coefficient and the information divergence, respectively. Subsequently, RRI combines these three indicators as the subset evaluation criterion and utilizes immune clone selection algorithm to search for the desired band subset. Experimental results verify that the proposed RRI method can provide higher classification accuracy than the competitors and is robust to noisy bands.

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is essential for infrared search and tracking systems. In this letter, we establish a multiple small targets detection method derived from hierarchical maximal entropy random walk (HMERW). The HMERW revolves the limitation of strong bias to the most salient target of the primal maximal entropy random walk (MERW) based on a proposed graph decomposition theory. To enhance the characteristics of small targets and suppress strong clutters, we design a specific weight matrix for HMERW instead of using the conventional weight matrix in MERW. First, a stationary distribution map is obtained by importing the filtered infrared image into the HMERW. Second, a coefficient map is constructed based on the designed weight matrix to fuse the stationary distribution map. Then, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical datasets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression, and multiple small targets detection.

Despite that 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 article proposes a novel IR small target detection method named improved multimode 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 the 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 multimode 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 the structure tensor theory, we introduce a new sparse prior map called 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 tubewise sparse regularization term is designed to identify linear sparse structures. The Frobenius norm is used to characterize noise. Finally, to solve the proposed model, an efficient optimization scheme utilizing the alternating direction method of multipliers (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 (IRSTD) is a challenging task, due to the scarce target feature, complex background interferences, and poor image quality. The existing studies handle the IRSTD problem by making specific distribution assumptions of target and background, which incurs two issues. First, the specific assumptions of background cannot always hold. Second, the distorted target distributions contaminated by heavy clutters and noise are difficult to model statically. This paper handles the problems from the discriminative and dynamic perspectives, and proposes an original mechanism named dynamic image structure evolution (DISE) and an DISE-derived single-frame IRSTD framework. First, we resolve IRSTD by a discriminative model without assuming specific background distributions, which is based on a mathematical definition of structure singularity modeling the ideal target structure. Second, to decouple the target distributions from interferences, DISE guides the distorted target to reveal potential structure singularity while suppress the interference signal through iterative procedures of structure collapse, intensity settlement, and collapse convergence. The three functional procedures of DISE perform their own duties regarding target enhancement and background suppression. Moreover, an innovative chain mechanism is introduced to propagate the structure field. Experiments on real data sets demonstrate the superiority of DISE against the state-of-the-art IRSTD 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.