High Dynamic Range (HDR) imaging endeavors to enhance the visual appeal of an image by merging multi-exposure images. However, images captured with hand-held cameras often suffer from severe motion blur and ghosting artifacts in dynamic scenes. To address these challenges, we propose Image Deblurring and HDR Imaging (ID-HDRI), an end-to-end joint optimization framework that directly recovers sharp details and reconstructs HDR images from blurred, multi-exposure inputs. The proposed framework employs a dual-branch architecture that divides the encoder into two individual branches, each dedicated to a specific task. This innovative design facilitates the separate learning of degradation features. Furthermore, to fully utilize the potential of these features, we introduce multi-scale fusion and gate fusion modules, which play key roles in the entire network. Additionally, we build a new dataset comprising images with varying exposure levels and blurriness for HDR imaging in dynamic settings. Finally, ablation analyses demonstrate the efficacy of these fusion modules in enhancing HDR image reconstruction performance. Compared to traditional pipelines that sequentially perform CNN-based deblurring and HDR imaging, our method exhibits superior performance in both objective metrics and subjective visual quality

With the development of space laser communication and the planned deployment of satellite Internet constellations, there is a growing demand for microminiature laser communication terminals. To meet the requirements of size, weight and power (SWaP), miniaturized terminals require smaller drive components to complete on-orbit scanning and capture, which must be fast and efficient to enable satellite laser communication networks. These miniaturized laser communication terminals are highly susceptible to the impact of the initial pointing accuracy of the laser beam and microvibrations of the satellite platform. Therefore, this paper proposes a laser scanning-capture model based on a Micro-electromechanical Systems (MEMS) micromirror that can provide a fast, large-scale scanning analysis. A scanning overlap factor is introduced to improve the capture probability under the influence of microvibrations. Finally, experimental analysis was carried out to verify the effectiveness of the proposed model, which can establish a theoretical basis for future ultra-long-distance microspace laser communication.

Few-shot learning (FSL) is an effective approach to address the issue of limited labeled data in hyperspectral image classification (HSIC). However, it overlooks the domain shift between the source and target domains in cross-domain tasks. Most existing domain adaptation methods alleviate the domain shift problem to some extent, but domain adaptation methods based on traditional convolutional operators overlook the non-local spatial relationships in HSI, while methods based on graph neural networks (GNNs), although effective in leveraging non-local spatial information for domain alignment, overly emphasize global relationships, which is disadvantageous for pixel-level classification in HSI. To solve these issues, this paper propose a novel cross-domain few-shot learning framework based on global-local graph attention networks (GLGAT-CFSL), which comprehensively reduces domain shift through global-to-local domain alignment. It has the following advantages: (1) an innovative dynamic triplet graph attention network is devised to identify non-local spatial relationships in HSI for global graph alignment, while also addressing common over-fitting and over-smoothing issues in GNNs; (2) an ingenious local similarity learning strategy is designed after global domain alignment, utilizing intra-domain connectivity structures and inter-domain node similarities for local domain adaptation, promoting cross-domain information propagation and more comprehensive reduction of domain shift; (3) we propose a novel triaxial dynamic convolutional neural network as the feature extractor, promoting cross-dimensional interaction between spectral and spatial dimensions, establishing a more generalizable and rich feature representation between the source and target domains. Experimental results on three HSI datasets demonstrate the superiority and effectiveness of the proposed GLGAT-CFSL.

publicly available.High Dynamic Range (HDR) imaging aims to reconstruct scenes with a wide range of luminance by fusing multi-exposure Low Dynamic Range (LDR) images. In dynamic scenes with pronounced foreground motion or camera jitter, especially under challenging conditions including extremely low or high luminance, widespread saturation, and substantial motion, existing approaches often encounter ghosting artifacts, spatial misalignment, and degradation of fine structural details. Traditional techniques based on handcrafted priors struggle to generalize to complex motion patterns, while most deep learning-based methods operate exclusively in the spatial domain, limiting their ability to capture global contextual cues and restore high-frequency structures that are better represented in the frequency domain. To address these challenges, we introduce a Dual-Domain Parallel Fusion Network with Prompt Refinement (DDPF-PR), which jointly leverages spatial and frequency-domain features for enhanced HDR reconstruction. Specifically, the proposed framework consists of a Bi-Domain Interaction Module(BDIM), which integrates spatial features for local detail and frequency features for global structure to suppress ghosting artifacts caused by motion. In addition, a Prompt Refinement Module(PRM) is designed to recover fine details in degraded regions such as saturated or misaligned areas by adaptively generating structural cues. Extensive experiments demonstrate that DDPF-PR consistently outperforms state-of-the-art methods across multiple benchmarks in both qualitative and quantitative evaluations. The code will be made publicly available.

In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 μrad (σ) to 2.2 μrad (σ), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications.

Synthetic Aperture Radar (SAR) is widely used in terrain classification, object detection, and other fields. Compared with anchor-based detectors, anchor-free detectors remove the anchor mechanism and implement detection box encoding in a more elegant form. However, anchor-free detectors are limited by complex scenes caused by geometric transformations such as overlay, shadow, vertex displacement during SAR imaging. And the scattered power distribution of noise is similar to the edge of the object, making it difficult for the detector to locate the edge of the SAR object accurately. In order to alleviate these problems, we propose a high-speed and high-performance SAR image anchor-free detector. First, we propose a shallow feature refinement (SFR) module to effectively extract and retain the detailed information of objects while coping with deformed complex scenes. Second, we analyze the optimization focus of the detector at different training iterations and propose Iteration-Aware Loss to guide the detector, making the detector more accurately locate the edge of the object disturbed by the noise scattered power distribution. Third, number estimation helps to detect objects with more flexible criteria in box selection without manual labor. Compared with mainstream optical object detectors and SAR dedicated detectors, our method achieves the best speed-accuracy trade-off on the SAR-Ship dataset, with 96.4% Average Precision when the value of Intersection over Union (IoU) is 50% AP50 at 64.9 Frames Per Second (FPS). The experimental results prove the effectiveness of our method.