Space-based infrared target detection can provide full-time and full-weather observation of targets, thus it is of significance in space security. However, the presence of stars in the background can severely affect the accuracy and real-time performance of infrared dim and small target detection, making star suppression a key technology and hot spot in the field of space target detection. The existing star suppression algorithms are all oriented towards the detection before track method and rely on the single image properties of the stars. They can only effectively suppress bright stars with a high signal-to-noise ratio (SNR). To address this problem, we propose a new method for infrared dim star background suppression based on recursive moving target indication (RMTI). Our proposed method is based on a more direct analysis of the image sequence itself, which will lead to more robust and accurate background suppression. The method first obtains the motion information of stars through satellite motion or key star registration. Then, the advanced RMTI algorithm is used to enhance the stars in the image. Finally, the mask of suppressing stars is generated by an accumulation frame adaptive threshold. The experimental results show that the algorithm has a less than 8.73% leakage suppression rate for stars with an SNR ≤ 2 and a false suppression rate of less than 2.3%. The validity of the proposed method is verified in real data. Compared with the existing methods, the method proposed in this paper can stably suppress stars with a lower SNR.

An infrared camera is affected by the photon effect, temperature changes, time drift and other factors when operating in orbit, which makes the ground non-uniformity correction coefficient invalid, resulting in non-uniformity stripes in the infrared images and restricting their practicality in further analysis and applications. Existing destriping methods often suffer from loss of image details and artifact generation. To solve this problem, we proposed a weighted guided filtering-based multi-domain fusion destriping approach that leverages the structural, directional, and spectral characteristics of stripe noise. Firstly, we addressed the issue of artifacts caused by Fourier domain filtering through an adaptive filtering approach that employs a variable threshold to minimize filtering induced artifacts and obtain clearer guided images. Furthermore, capitalizing on the directional properties of wavelet decomposition effectively separates image information from stripe information. To integrate the advantages of both approaches, we employed a weighted guided filter to seamlessly fuse the guided image with the wavelet decomposition image. In terms of quantitative metrics, the proposed method generally beats the other five comparative methods, with significant improvements in image PSNR, SSIM, NIQE, and MRD, particularly for complex images where the enhancements were more pronounced. These experimental results collectively demonstrate the significant progress achieved by the proposed method in effectively reducing stripe noise, better preserving the original structural details of the image, and suppressing the occurrence of artifacts.

In infrared detectors, the readout circuits usually cause horizontal or vertical streak noise, whereas the infrared focal plane arrays experience triangular nonuniform fixed-pattern noise. In addition, imaging devices suffer from optically relevant fixed-pattern noise owing to the temperature. When the infrared camera is in orbit, it is affected by the photon effect, temperature change, and time drift. This makes the nonuniformity correction coefficients pertaining to the ground no longer applicable, resulting in the degradation of the nonuniformity correction effect. The existing methods are not fully applicable to triangular fixed-pattern noise or the fixed-pattern noise caused by detector optics. To address this situation, this paper proposes a nonuniformity correction method, namely infrared image sequences based on the optimization of $L_{2,1}$ group sparsity in the spatiotemporal domain. We established a nonuniformity correction model of differential operators in the spatiotemporal domain for infrared image sequences by applying the time-domain differential operator constraints to the images to denoise the image. This enables the adaptive correction of the nonuniformity of the above types of noise. We demonstrate that the proposed method is effective for triangular nonuniform and optically induced fixed-pattern noises. The proposed method was extensively evaluated using publicly available datasets and datasets containing image sequences of different scenes captured by a high-resolution infrared camera of the Qilu-2 satellite. The method has high robustness and good processing results with effective ghost suppression and significant reduction of nonuniform noise.

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands.