Full-sky autonomous star identification is one of the key technologies in the research of star sensors. As one of the classical pattern-based star identification methods, the Grid algorithm has shown promising performance. Na further modified it to improve robustness to position noise. However, the inherent alignment star mismatch and pattern inconsistency are still not solved. To address these problems, we propose a novel star identification method. Specifically, we design distance-guided redundant-coded patterns for different alignment stars to alleviate the problem of alignment star mismatch. Then, we create a masked grid pattern to address the inconsistency between the sensor pattern and the catalog pattern. Distances of the reference stars to their corresponding alignment stars are adopted to assist in choosing the correct alignment star, as well as reducing the number of catalog patterns that need to be evaluated. Experimental results on both synthesized and night sky images show that the proposed algorithm is quite robust to false stars, position noise, and magnitude noise. The identification accuracy of this algorithm is 98.43% with standard deviations of position noises is 2.0 pixels and 98.52% with standard deviations of magnitude noises is 0.5 Mv. Moreover, the algorithm obtains an average identification accuracy of 99.6% from night sky images.

The effectiveness of infrared small target detection technology depends on high detection accuracy. However, when dealing with complex backgrounds or targets close to the edges of jamming objects, existing algorithms typically encounter significant challenges, such as high false alarm rates and low detection probabilities. To address this issue, we propose a novel measurement scheme utilizing joint local contrast measure based on background enhancement and edge suppression (JLCM-BEES), in which a new scanning window is employed to compute the joint local contrast of infrared images, with the focus on enhancing the target while suppressing the background, a new filtering kernel strengthens the background and suppresses the target, and two related structural elements are used for dilation and differencing operations on the infrared image, retaining the target while effectively suppressing edges with strong contrast. A new fusion method is introduced to combine the three obtained saliency maps, followed by the separation of the target from the background using an adaptive thresholding algorithm. Experimental results indicate that the proposed algorithm demonstrates superior detection capability and robustness when facing complex conditions and low signal-to-clutter ratios, providing new insights for the development of infrared small target detection technology.

Correlation filter (CF) based trackers have recently drawn great attention in visual tracking community due to their impressive performance and computational efficiency on benchmark datasets. However, the performance of most existing trackers using correlation filter is hampered by two aspects: i) Included background information in the selected rectangular target patch is considered as part of the target, and they are treated as important as the real target in training new filter model, it causes the filter easily drift when target shape changes dramatically. ii) Existing filters use a moving average operation with an empirical weight to update the filter model in each frame, such per frame adaptation constantly introduces new information of the target patch, but never consider the consistence of the historical information and the newly obtained one, further increases the risk of drifting. This paper presents a new framework including saliency map and a novel CF regression model. We reformulate the original optimization problem, and provide a closed form solution for multidimensional features which is solved efficiently using alternating direction method of multipliers (ADMM) and accelerated using Sherman-Morrison lemma, our algorithm as a new framework can be easily integrated into CF base trackers to boost their tracking performance. We perform comprehensive experiments on five benchmarks: OTB-2015, VOT2016, VOT2018, UAV123, and TempleColor-128. Results show that the proposed method performs favorably against lots of state-of-the-art methods with a speed close to real-time. Our method with deep features performs much better on all 5 datasets. Our code will be released to facilitate further researches.

Imaging in the atmosphere presents the phenomenons of low contrast, low saturation and hue offset due to atmospheric particles such as haze and fog. In this paper, an optimized design method is proposed to remove haze from a single image. Based on the atmospheric scattering model, dark channel prior is used to estimate the value of global atmospheric light by an interval. Meanwhile, the initial estimation of medium transmission is obtained. And the white balance is performed to simplify the atmospheric scattering model. Then, to deal with the invalid case of the dark channel, we exploit a scheme to identify bright area based on dual threshold and develop a way to correct transmission rate, which enables the dark channel prior to be more applicable. Finally, enhanced processing is introduced by adopting nonlinear contrast stretching, which improves the visual effect of dehazing images. The experimental results show that the proposed method can achieve a faster processing speed, effectively improve the visibility and contrast of the restored image, and obtain good color effect.

Pointing at the defect of Space Frequency Algorithm in abstracting detail information used in Regional Feature Self-Adaptive Image Fusion Algorithm, an improved Spatial Frequency Algorithm is proposed. Firstly, the histogram specification is used to adjust pixel values of the infrared image. Secondly, the multi-scale decomposition is performed on visible image and the adjusted infrared image using Contourlet transform. Finally, low-frequency sub-band is fused using a rule based on Regional Energy rather than the simple weighted sum method. Other sub-bands are fused using the Improve Spatial Frequency rule. The experimental results show that the proposed method preserves more details of the visible image and obtains clear target information in the infrared image. A better effect is achieved compared to the reference article.

Star trackers are susceptible to interference from stray light, such as sunlight, moonlight, and Earth atmosphere light, in the space environment, resulting in an overall improvement in the star image grayscale, poor background uniformity, low star extraction rate, and high number of false star spots. In response to these challenges, this paper proposes a grayscale iterative star spot extraction algorithm based on image entropy. The implementation of the algorithm is mainly divided into two steps: (1) The algorithm conducts multiple grayscale iterations, effectively utilizing the prior information on the local contrast of star spots to filter out stray light backgrounds to a certain extent. (2) By establishing an inner–outer template, the image entropy algorithm is employed to obtain the real star targets to be extracted, which further suppresses the background clutter and noise. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this algorithm can effectively suppress background stray light, enhance star extraction rates, and reduce the number of false star spots, and it exhibits superior detection performance in complex backgrounds across various scenarios.