Reaction-boned silicon carbide (RB-SiC) is considered a new material for large lightweight ground-based space telescopes due to its high specific stiffness, low thermal deformation, and excellent optical quality. The excellent mechanical properties of RB-SiC result in the low efficiency of traditional polishing and mechanical polishing. In this paper, a polishing method for RB-SiC based on a femtosecond laser is proposed to improve surface quality. A theoretical heat conduction model was established in the process of femtosecond laser irradiation of SiC. We analyzed the ablation type and calculated the single-pulse ablation threshold of SiC, which verified the feasibility of femtosecond laser polishing. Further, the effects of polishing parameters on the polished surface quality were analyzed by a series of experiments, and the optimal parameters were selected. It was observed to improve polishing efficiency and can replace the intermediate steps of traditional mechanical polishing.

High-quality space target images are important for space surveillance and space attack defense confrontation. To obtain space target images with higher resolution and sharpness, this paper proposes an image super-resolution reconstruction network based on dual regression and a deformable convolutional attention mechanism (DCAM). Firstly, the mapping space is constrained by dual regression; secondly, deformable convolution is used to expand the perceptual field and extract the high-frequency features of the image; finally, the convolutional attention mechanism is used to calculate the saliency of the channel domain and the spatial domain of the image to enhance the useful features and suppress the useless feature responses. The experimental results show that the method outperforms the comparison algorithm in both objective quality evaluation index and localization accuracy on the space target image dataset compared with the current mainstream image super-resolution algorithms.

The sCMOS imaging system with deep vacuum cooling technology has become a necessary way to improve the detection capability of space targets. In order to improve the detection capability of the photoelectric detection equipment for space targets, this paper developed the Highly Sensitive Deep Vacuum Cooling Imaging System (HSDVCIS). Firstly, we designed the imaging readout processing circuit using the GSENSE4040 sCMOS image sensor designed and manufactured by Gpixel and the deep vacuum cooling structure using thermoelectric cooling. Then, we tested the designed HSDVCIS with readout noise, dark current, and dynamic range of 3.96 $e^{-}$, 0.12 $e^{-}$/pixel/sec, and 84.49 dB, respectively, and tested the image sensor with a minimum cooling temperature of −40 °C. Finally, according to the results of observation experiments, we validated that the photoelectric detection equipment equipped with HSDVCIS improved the limiting detection magnitude (at SNR = 5 level) from 13.22 to 13.51 magnitudes within a 3 s exposure time by turning on the cooling function. Therefore, HSDVCIS designed in this paper can achieve highly sensitive detection of space targets. At the same time, the development of HSDVCIS also provides technical reserves and strong support for future research on the imaging systems using multiple image sensor mosaics.

High-resolution imaging with wide field of view (FoV) ground-based telescopes is often affected by skylight background and noise due to the detector, resulting in an inhomogeneous background. In this paper, we propose an improved method for spatial image non-uniformity correction based on partition processing. First, an evaluation metric is introduced to evaluate the partition size and automatically iterate a suitable partition value for different scenarios based on the different operating conditions of the telescope. Then, we iteratively calculate the mean and variance in each partitioned region to extract the background of each partitioned region. Finally, after applying bilinear interpolation to the background extracted from each region, the inhomogeneous background is obtained and removed from the original image. The experiments on the simulated and real images show that the proposed method can effectively remove the inhomogeneous background of spatial images and meet the requirements of the real-time processing of high-resolution images under long exposure conditions.