Small target detection against strong vignetting background poses two major challenges: the submergence of the target in high brightness background and the difficult separability between dense noise and weak target. These challenges have not been adequately addressed by existing methods, which heavily rely on the low-rank prior of background. In this paper, our focus shifts to the smoothness of background and propose a dynamic weight guided smooth-sparse decomposition model (DW-SSD). First, we adopt a surface-fitting perspective to reconstruct the smooth vignetting, where B-spline is introduced as the basis, and total variation (TV) is utilized to regularize the coefficients of the B-spline basis. This term provides a flexible description for different background local regions with low computation cost, effectively suppressing the high brightness vignetting and facilitating the emergence of target. Second, a dynamic weight based on spatial-temporal prior characteristic is added to the target component, where the differences in spatial contrast and temporal profile are jointly considered to guide a precise separation of the target from dense noise. Finally, a customized solution is designed based on the alternating direction multiplier method (ADMM) to separate small targets from strong vignetting rapidly and precisely. Comprehensive experiments on five real sequences demonstrate that DW-SSD method effectively overcomes the challenges posed by strong vignetting and performs well in real-time small target detection. The code is publicly available at https://github.com/D-IceIce/DW-SSD.

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.

Star identification plays a key role in spacecraft attitude measurement. Currently, most star identification algorithms tend to perform well only in a scene without noise and are highly sensitive to noise. To solve this problem, this paper proposes a star identification algorithm based on the maximum spanning tree (MST) index and multi-order continuous cycle angle (CCA) intended for the lost-in-space mode. In addition, a neighboring star selection method named dynamic eight-quadrant (DEQ) is developed. First, the DEQ method is used to select high-confidence neighboring stars for the main star. Then, the star image is regarded as a graph, and the Prim algorithm is employed to construct the MST pattern for each guide star, which is then combined with the K vector index to perform the main star candidate search. Finally, the Jackard similarity voting for the multi-order CCA of the main star is used to identify the main star, and the global neighboring star identification is conducted by the multi-order CCA of neighboring stars. The simulated and real star images test results show that compared with five mainstream algorithms, when the position noise is 1 pixel, the number of false stars is five, the magnitude noise is 0.5, and the identification accuracy of the proposed algorithm is higher than 98.5%. Therefore, the proposed algorithm has excellent anti-noise ability in comparison to other algorithms.

Stochastic resonance (SR) is usually utilized to enhance the signal with the help of noise. Inspired by this, we find the SR can also handle the problem of dim-small target detection under the low local signal-to-noise ratio (LSNR) situation. In this letter, we propose a novel spatial-temporal stochastic resonance (STSR) model for dim-small target detection. First, we select the SR as the core model to enhance the salience of the target by the inherent strong noise. With the help of the Poisson distribution prior, we employ the multiple adjacent frames as the input of the SR model, improving the LSNR of the resonance state through the temporal accumulation of photons. Then, we introduce the total variation regularization in the variational framework to remove the false alarm points by spatial smoothing, while preserving the role of noise in the SR. Finally, we customize an optimization process based on the alternating direction method of multiplier (ADMM) to solve the STSR variational minimization problem. Both the qualitative and quantitative experiments on real visible and infrared image sequences have demonstrated the superiority of the proposed model, especially in the low LSNR situation below 2 dB.

The construction of a guide star catalog is crucial for a star sensor to achieve accurate star map recognition and attitude determination. At present, the methods of a guide star catalog for a large field of view (FOV) star sensor have been relatively mature. However, for a small-FOV star sensor, there are still certain problems, such as a large storage capacity of a guide star catalog, uneven distribution of stars, and easy occurrence of voids. To address these problems, we propose a construction method of a small-FOV star sensor guide star catalog based on the combination of the improved spherical spiral method (ISSM) and extreme learning machine (ELM) named the ISSM-ELM. First, a spiral reference point is used as an optical axis pointing of the star sensor, and the guide stars are preliminarily screened based on the star-diagonal distance between the star and the reference point, and the star-density and magnitude characteristics of the guide star. Then the ELM is used to supplement the guide star empty sky area to construct an integrity guide star catalog. The experimental results demonstrate that the proposed method can reduce the storage capacity of the guide star catalog and improve its uniformity, integrity, and average brightness.