Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

Infrared small target detection is a critical task in remote sensing applications, such as aerial reconnaissance, maritime surveillance, and early-warning systems. However, due to the inherent characteristics of remote sensing imagery, such as complex backgrounds, low contrast, and limited spatial resolution-detecting small-scale, dim infrared targets remains highly challenging. To address these issues, we propose MFAFNet, a novel Multi-Feature Attention Fusion Network tailored for infrared remote sensing scenarios. The network comprises three key modules: a Feature Interactive Fusion Module (FIFM), a Patch Attention Block (PAB), and an Asymmetric Contextual Fusion Module (ACFM). FIFM enhances target saliency by integrating the original infrared image with two locally enhanced feature maps capturing different receptive field scales. PAB exploits global contextual relationships by computing inter-pixel correlations across multi-scale patches, thus improving detection robustness in cluttered remote scenes. ACFM further refines feature representation by combining shallow spatial details with deep semantic cues, alleviating semantic gaps across feature hierarchies. Experimental results on two public remote sensing datasets, SIRST-Aug and IRSTD-1k, demonstrate that MFAFNet achieves excellent performance, with mean IoU values of 0.7465 and 0.6701, respectively, confirming its effectiveness and generalizability in infrared remote sensing image analysis.

Weakly-supervised object detection (WSOD) methods that trains an object detection network using image-level labels has attracted much attention due to its cost-effective annotation and broad applied requirement. However, applying such weak label to detect targets in infrared images is not trivial due to the less discriminative target information and interference of complex backgrounds. This paper proposes a Multi-grained and Confidence-aware Multiple Instance Network (MCMIN) to detect infrared targets given the imprecise labels. The multi-scale multi-grained feature extraction module is designed to capture discriminative features from different receptive fields for dim-small targets. The hierarchical multiple instance target detection module firstly applies L1-sparsity regularization to encourage the model generate reliable pseudo ground truth, and then leverages the confidence-aware instance adaptive weighting strategy to refine proposals with particular emphasis, achieving more accurate target detection. The experimental results on two infrared target detection datasets illustrate that the proposed MCMIN outperforms other state-of-the-art WSOD methods with higher AP. The proposed approach decreases the false alarms.

Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.

Short-wave infrared imaging technology has rapidly developed over the past decade. However, image detail and contrast enhancement techniques, which are crucial for infrared imaging systems, are rarely dedicated to SWIR imaging systems. Moreover, the existing IR image enhancement methods generally have difficulty providing real-time performance, which plays a significant role in imaging systems with high data rates. In this paper, we propose a simple and real-time SWIR image enhancement approach based on the difference of Gaussian filter and histogram remapping, and illustrate the implementation of the proposed method on FPGA. The experimental results demonstrated that our method achieves promising detail and contrast enhancement performance with a frame rate of around 50 fps for high-definition images, and that extremely high frame rates can be achieved with pipelined architecture.