Cheng Li

The detection of small targets in infrared imaging has always been challenging due to their low area ratio and the presence of complex backgrounds. In this work, we propose a multi-scale tight junction network and deep data fusion paradigm for better detection performance. First, the multi-scale tight junction network could effectively capture the diverse features of infrared small targets at various scales through feature extraction fusion and feature reusing via a dense connection structure. It addresses the issue of information loss in conventional methods and allows for a comprehensive exploration of potential features across different scales. In addition, the introduced deep data fusion paradigm incorporates a multi-dimensional slightly variable weight fusion strategy and an ordered weighted fusion strategy at the decision-making level. The former strategy deals with the different dimensional characteristics of multi-scale networks to achieve multi-dimensional integration. This is done to highlight the characteristics of small targets and enhance the stability and generalization ability. Meanwhile, the latter comprehensively evaluates the detection results at the decision-making level to improve the rationality and effectiveness of fusion. To further validate its effectiveness, we conducted numerous experiments on a public infrared small target dataset and compared it with several advanced algorithms. Experimental results demonstrate significant improvements in several popular metrics compared with the state-of-the-art algorithms. Notably, it accurately detects small targets in complex background scenarios with low contrast while effectively reducing false and missed detections. As an effective solution for infrared small target detection, the proposed work shows significant theoretical value and broad application prospects.

Recently, hyperspectral image (HSI) mixed denoising methods based on nonlocal subspace representation (NSR) have achieved significant success. However, most of these methods focus on optimizing the denoiser for representation coefficient images (RCIs) without considering how to construct RCIs that better inherit the spatial structure of the clean HSI, thereby affecting subsequent denoising performance. Although existing works have constructed RCIs from the perspective of sparse principal component analysis (SPCA), the refinement of RCIs in mixed noise conditions still leaves much to be desired. To address the aforementioned challenges, in this paper, we reconstructed robust RCIs based on SPCA in mixed noise circumstances to better preserve the spatial structure of the clean HSI. Furthermore, we propose to utilize the robust RCIs as prior information and perform iterative denoising in the denoiser that incorporates low-rank approximation. Extensive experiments conducted on both simulated and real HSI datasets demonstrate that the proposed robust RCIs guidance and low-rank approximation method, denoted as RRGNLA, exhibits competitive performance in terms of mixed denoising accuracy and computational efficiency. For instance, on the Washington DC Mall (WDC) dataset in Case 3, the denoising quantitative metrics of the mean peak signal-to-noise ratio (MPSNR), mean structural similarity index (MSSIM), and spectral angle mean (SAM) are 36.06 dB, 0.963, and 3.449, respectively, with a running time of 35.24 s. On the Pavia University (PaU) dataset in Case 4, the denoising quantitative metrics of MPSNR, MSSIM, and SAM are 34.34 dB, 0.924, and 5.505, respectively, with a running time of 32.79 s.

Detecting non‐functional satellite components is critical for on‐orbit servicing. Current detection methods struggle with complex image noise, motion blur in space environments, and the limited realism of artificially synthesised sample data. To address these challenges, we propose an enhanced you only look once version 8 (YOLOv8)‐based method. In terms of network architecture, we introduce innovative designs for the backbone and neck components. A novel hybrid attention mechanism replaces the conventional approach, improving the perception and processing of intricate image features and significantly enhancing feature extraction. Additionally, we integrate modules inspired by residual networks into the neck structure, improving training adaptability and ensuring robust information transmission. This design highlights key target features while minimising feature attenuation. We also establish the satellite key element (SAKE) dataset under simulated real space conditions, including image noise and jitter blur. This dataset features components such as satellite bodies and solar panels and uses an encoder–decoder network architecture to refine context information. By merging this with a branch preserving high‐resolution details, we enhance dataset expressiveness. Experiments demonstrate that the enhanced algorithm achieves a mean average precision (mAP) of 78.98% on the SAKE dataset, a 2.57% improvement over the original YOLOv8. The refined model effectively detects critical satellite components, showing superior performance in noisy and blurry scenarios.

As an extremely efficient preprocessing tool, superpixels have become more and more popular in various computer vision tasks. Nevertheless, there are still several drawbacks in the application of hyperspectral image (HSl) processing. Firstly, it is difficult to directly apply superpixels because of the high dimension of HSl information. Secondly, existing superpixel algorithms cannot accurately classify the HSl objects due to multi-scale feature categorization. For the processing of high-dimensional problems, we use the principle of PCA to extract three principal components from numerous bands to form three-channel images. In this paper, a novel superpixel algorithm called Seed Extend by Entropy Density (SEED) is proposed to alleviate the seed point redundancy caused by the diversified content of HSl. It also focuses on breaking the dilemma of manually setting the number of superpixels to overcome the difficulty of classification imprecision caused by multi-scale targets. Next, a space–spectrum constraint model, termed Hyperspectral Image Classification via superpixels and manifold learning (SMALE), is designed, which integrates the proposed SEED to generate a dimensionality reduction framework. By making full use of spatial context information in the process of unsupervised dimension reduction, it could effectively improve the performance of HSl classification. Experimental results show that the proposed SEED could effectively promote the classification accuracy of HSI. Meanwhile, the integrated SMALE model outperforms existing algorithms on public datasets in terms of several quantitative metrics.

The superpixel usually serves as a region-level feature in various image processing tasks, and is known for segmentation accuracy, spatial compactness and running efficiency. However, since these properties are intrinsically incompatible, there is still a compromise within the overall performance of existing superpixel algorithms. In this work, the property constraint in superpixels is relaxed by in-depth understanding of the image content, and a novel two-stage superpixel generation framework is proposed to produce content-aware superpixels. In the global processing stage, a diffusion-based online average clustering framework is introduced to efficiently aggregate image pixels into multiple superpixel candidates according to color and spatial information. During this process, a centroid relocation strategy is established to dynamically guide the region updating. According to the area feature in manifold space, several superpixel centroids are then split or merged to optimize the regional representation of image content. Subsequently, local updating is adopted on pixels in those superpixel regions to further improve the performance. As a result, the dynamic centroid relocating strategy offers online averaging clustering the property of content awareness through coarse-to-fine label updating. Extensive experiments verify that the produced superpixels achieve desirable and comprehensive performance on boundary adherence, visual satisfactory and time consumption. The quantitative results are on par with existing state-of-the-art algorithms in terms with several common property metrics.

Infrared image enhancement technology plays a crucial role in improving image quality, addressing issues like low contrast, lack of sharpness, and poor visual effects within the original images. However, existing decomposition-based algorithms struggle with balancing detail enhancement, noise suppression, and utilizing global and local information effectively. This paper proposes an innovative method for enhancing details in infrared images using adaptive guided filtering and global–local mapping. Initially, the original image is decomposed into its base layer and detail layer through the adaptive guided filter and difference of the Gaussian filter. Subsequently, the detail layer undergoes enhancement using a detail gain factor. Finally, the base layer and enhanced detail layer are merged and remapped to a lower gray level. Experimental results demonstrate significant improvements in global and local contrast, as well as sharpness, with an average gradient enhancement of nearly 3% across various scenes.

Superpixel segmentation is a popular preprocessing tool in the field of image processing. Nevertheless, conventional planar superpixel generation algorithms are inadequately suited for segmenting symmetrical spherical images due to the distinctive geometric differences. In this paper, we present a novel superpixel algorithm termed context identity and contour intensity (CICI) that is specifically tailored for spherical scene segmentation. By defining a neighborhood range and regional context identity, we propose a symmetrical spherical seed-sampling method to optimize both the quantity and distribution of seeds, achieving evenly distributed seeds across the panoramic surface. Additionally, we integrate the contour prior to superpixel correlation measurements, which could significantly enhance boundary adherence across different scales. By implementing the two-fold optimizations on the non-iterative clustering framework, we achieve synergistic CICI to generate higher-quality superpixels. Extensive experiments on the public dataset confirm that our work outperforms the baselines and achieves comparable results with state-of-the-art superpixel algorithms in terms of several quantitative metrics.

Leveraging space-based optical platforms for space debris and defunct spacecraft detection presents several advantages, including a wide detection range, immunity to cloud cover, and the ability to maintain continuous surveillance on space targets. As a result, it has become an essential approach for accomplishing tasks related to space situational awareness. However, the prediction of the orbits of space objects is crucial for the success of such missions, and current technologies face challenges related to accuracy, reliability, and practical efficiency. These challenges limit the performance of space-based optical space situational awareness systems. To drive progress in this field and establish a more effective and reliable space situational awareness system based on space optical platforms, this paper conducts a retrospective overview of research advancements in this area. It explores the research landscape of orbit determination methods, encompassing orbit association methods, initial orbit determination methods, and precise orbit determination methods, providing insights from international perspectives. The article concludes by highlighting key research areas, challenges, and future trends in current space situational awareness systems and orbit determination methods.

The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. Nevertheless, existing satellite image datasets exhibit several deficiencies, such as the lack of satellite motion states, extreme illuminations, or occlusion of critical components, which severely hinder the performance of detection algorithms. In this work, we bridge the gap via the release of a novel dataset tailored for the detection of key components of satellites. Unlike the conventional datasets composed of synthetic images, the proposed Typical Components of Satellites (TYCOS) dataset comprises authentic photos captured in a simulated space environment. It encompasses three types of satellite, three types of key components, three types of illumination, and three types of motion state. Meanwhile, scenarios with occlusion in front of the satellite are also taken into consideration. On the basis of TYCOS, several state-of-the-art detection methods are employed in rigorous experiments followed by a comprehensive analysis, which further enhances the development of space scene perception and satellite safety.

Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method.