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.

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations.

Single-pixel imaging can reconstruct the original image at a low measurement rate (MR), and the target can be measured and reconstructed in low-light environments by capturing the light intensity information using a single-photon detector. Optimizing reconstruction results at low MR has become a focal point of research aimed at enhancing measurement efficiency. The application of neural network has significantly improved reconstruction quality, but the performance still requires further enhancement. In this paper, a Diffusion Single Pixel Imaging Model (DSPIM) method is proposed. The conditional diffusion model is utilized in the training and reconstruction processes of single-pixel imaging and is jointly optimized with an autoencoder network. This approach simulates the measurement and preliminary reconstruction of images, which are incorporated into the diffusion process as conditions. The noises and features are learned through a designed loss function that consists of predicted noise loss and measurement accuracy loss, allowing the reconstruction to perform well at very low MR. Besides, an adaptive regularization coefficients adjustment method (ARCA) has been designed for more effective optimization. Finally, the learned weights are loaded into the single photon counting system as a measurement matrix, demonstrating that the blurriness caused by insufficient features at low MR is effectively addressed using our methods, resulting in clearer targets and well-distinguished features.

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.

Traditional zoom lenses cannot clearly image during the entire zoom process when the ambient temperature changes and needs to focus frequently at middle focal length positions. An innovative design method called the optical passive semi-athermalization (OPSA) design for zoom optical systems is proposed which, based on the difference in the focusing sensitivity of the focusing group at short and long focal length positions, seeks out sensitive groups that have a greater impact on the imaging quality at the short focal position. By changing the temperature characteristics of the temperature-sensitive lenses in these groups, an OPSA zoom optical system can be realized, which exhibits a compact structure and excellent imaging quality. Under the ambient temperature of −40^∘C to +60^∘C, the OPSA zoom lens needs to refocus only once at the long focal length position, which can ensure an image clearly during the entire zoom process. Remarkably, this innovative method not only mitigates the frequent focusing challenges in traditional zoom lenses, but also contributes to the diminutive size.

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.