Infrared (IR) small target detection is gradually attracting a cornucopia of attention and developing rapidly in the application of infrared search and track (IRST) systems. However, it is often hard to extract small targets with adequate feasibility and reliability especially under a variety of different complex backgrounds. To improve the detection performance of small target embedded in sophisticated clutters, a novel hollow side window filter (HSWF) is designed to cope with the background estimation problem. Moreover, the saliency map constructed by heterogeneity filter is utilized as a critical weighting factor during the process of HSWF. Finally, with the help of commonly adopted patch contrast measure (PCM), the small target could be easily identified from hadamard product of the corresponding response maps. The effectiveness and outstanding performance of the proposed method are validated through comprehensive experiments and analysis.

Infrared small target detection is significantly challenged by residual high-intensity background edges and a low signal-to-noise ratio. These issues hinder accurate target differentiation from the background and heighten the risk of false alarms. To address these challenges, we propose a method which employs multidirectional Local Gravitational Force (LGF) contrast combined with Level-Line Connectivity (LLC) contrast. The LGF model integrates information from each pixel within the Local region and introduces a new sigmoid function to reduce noise, enabling fine-grained gradient detection. The magnitude and orientation in this gradient can then be used to differentiate the target from the background. Considering that the target exhibits different gradient features in different directions, we further propose a multidirectional LGF contrast. This contrast utilizes the distribution characteristics of LGF magnitude to enhance the target and effectively suppress strong edges. In addition, to fully utilize the orientation information in the LGF, we designed the LLC contrast based on the spatial consistency of the target, increasing the difference between the target and the background. Finally, we propose a regional fusion technique to weight the two contrasts, improving background suppression while preserving target intensity. Experimental results demonstrate the effectiveness of our method in detecting targets within high-intensity edge backgrounds, complex textures, and noisy environments. Compared to other state-of-the-art methods, our method significantly improves detection accuracy.

Infrared small target detection technology has sufficient applications in many engineering fields, such as infrared early warning, infrared tracking, and infrared reconnaissance. Due to the tiny size of the infrared small target and the lack of shape and texture information, existing methods often leave residuals or miss the target. To address these issues, a novel method based on a non-overlapping patch (NOP) joint l₀-l₁ norm is proposed with the introduction of sparsity regularized principal component pursuit (SRPCP). The NOP model makes the patch lighter in the first place, reducing time consumption. The adoption of the l₀ norm enhances the sparsity of the target, while the adoption of the l₁ norm enhances the robustness of the algorithm under clutter. As a smart optimization method, SRPCP solves the NOP model fittingly and achieves stable separation of low-rank and sparse components, thereby improving detection capacity while suppressing the background efficiently. The proposed method ultimately yielded favorable detection results. Adequate experiment results demonstrate that the proposed method is competitive in terms of background suppression and true target detection with respect to state-of-the-art methods. In addition, our method also reduces the computational time.

The robust detection of small targets is crucial in infrared (IR) search and tracking applications. Considering that many state-of-the-art (SOTA) methods are still unable to suppress various edges satisfactorily, especially under complex backgrounds, an effective infrared small target detection algorithm inspired by modified fast saliency and the weighted guided image filter (WGIF) is presented in this paper. Initially, the fast saliency map modulated by the steering kernel (SK) is calculated. Then, a set of edge-preserving smoothed images are produced by WGIF using different filter radii and regularization parameters. After that, utilizing the fuzzy sets technique, the background image is predicted reasonably according to the results of the saliency map and smoothed or non-smoothed images. Finally, the differential image is calculated by subtracting the predicted image from the original one, and IR small targets are detected through a simple thresholding. Experimental results on four sequences demonstrate that the proposed method can not only suppress background clutter effectively under strong edge interference but also detect targets accurately with a low false alarm rate.

Real-time dynamic star image registration is essential for star pattern recognition and target tracking. To ensure real-time registration within limited computing resources, a semi-analytical Fourier-Mellin method is introduced for estimating registration parameters. The proposed method uses the position information and connected-domain areas of extracted star points to calculate the Fourier spectra, in contrast to the conventional pixel-level Fourier transform. Consequently, the method demonstrates exceptional computational efficiency and resilience to amplitude fluctuations in deep-space backgrounds. Additionally, an innovative one-dimensional cross-correlation function is proposed for determining registration parameters, significantly reducing the number of search operations and computational complexity compared to traditional two-dimensional functions. Furthermore, a global coarse and local fine positioning strategy is implemented to enhance the framework's efficiency while maintaining registration accuracy. Numerical experiments validate that the proposed scheme constitutes a robust and real-time process for star image registration, making it a practical solution for on-orbit platforms with constrained computing and storage capabilities.

Optical image-based space debris detection is crucial for preventing collisions between spacecraft in orbit. However, identifying debris amidst low signal-to-noise ratios (SNR) with crowded stellar environment remains challenging. To enhance faint debris detection within complex backgrounds, this study proposes a multi-scale approach that leverages the morphological feature differences between tracked space debris and non-tracked stars for their identification. The proposed adaptive multi-scale morphological transformation demonstrates robustness against adjacent star and noise interference, achieving superior local background estimation for debris segmentation. Furthermore, we construct a feature representation based on the differential response of point-like and streak-like objects to morphological operations. This representation exhibits significant discriminative power for objects of differing morphology, facilitating their recognition. The proposed method offers a streamlined, one-step process for acquiring space debris, in contrast to the traditional methods of segmentation followed by classification. Numerical experiments confirm that the method surpasses traditional techniques, achieving higher detection rates and fewer false positives, particularly for debris within dense stellar backgrounds.