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14 August 2019 High-resolution imaging of space target based on compressed sensing
Congcong Yu, Hui Zhao, Ling Zhang, Jing Wang, Rui Ge, Xuewu Fan
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111793U (2019) https://doi.org/10.1117/12.2539647
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
Different degradation factors such as Poisson noise, blurring effect, different contrast and different reflectivity and so on will impose severe influences on the imaging process of the non-cooperative space targets with low light intensity and the corresponding image quality is usually poor. In this paper, a two-step reconstruction framework based on compressed sensing (CS) theory is proposed to deal with these degradation factors to improve the quality of the space target images. The proposed algorithm is divided into two steps, the first step is standard compressed sensing based reconstruction, and the second step is super-resolution based on the theory of compressed sensing. Specifically speaking, when the sparsely sampling are obtained, the total variation augmented Lagrangian alternating direction algorithm (TVAL3) is first used to recover the 2D image, which only obtain 25% of the number of pixels in the original image instead of all the pixels in the traditional sampling. Subsequently, the single-frame image super-resolution reconstruction is performed on the captured 2D image, and the super-resolution algorithm based on the dictionary learning is used to realize super-resolution reconstruction, which makes the image resolution doubled.
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Congcong Yu, Hui Zhao, Ling Zhang, Jing Wang, Rui Ge, and Xuewu Fan "High-resolution imaging of space target based on compressed sensing", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793U (14 August 2019); https://doi.org/10.1117/12.2539647
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KEYWORDS
Reconstruction algorithms
Compressed sensing
Detection and tracking algorithms
Super resolution
Image quality
Image resolution
Image restoration
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