Denoising star map data via sparse representation and dictionary learning

Zhou, Mingyuan; Shi, Ying; Yang, Jigang

doi:10.1016/j.ijleo.2015.02.091

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…a t β l retains only those items in non-zero locations of a v β l . Apply singular value decomposition (SVD) to E v [18]. Update the dictionary atoms 4. g = g + 1, return to step 1, each sample was iteratively calculated until the final dictionary A is obtained.…”

Section: The Residuals Matrixmentioning

confidence: 99%

“…Traditional de-noising methods can remove the noise but come at the cost of damaging the star image. Gai et al [17] and Zhou et al [18] proposed sparserepresentation-based methods for depressing noise in star images. These methods manage Gaussian noise effectively, but real astronomical images are polluted by the Poisson-Gaussian mixed noise [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Automatic centroid extraction method for noisy star image

Gou

Chen

2018

IET Image Processing

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Section: The Residuals Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic centroid extraction method for noisy star image

Gou

Chen

2018

IET Image Processing

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“…Arbabmir gave out an integrated scheme including noise reduction, star extraction, and star centroiding, which can improve the star acquisition accuracy under uneven illumination [7]. Zhou et al introduced a star map denoising method via spare representation and dictionary learning [8]. Jiang et al showed a robust and accurate star segmentation algorithm based on morphology, which can eliminate imaging noise effectively [9].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Autonomous Star Acquisition Method for Star Sensor under Complex Conditions

Zhang

Niu

et al. 2017

Mathematical Problems in Engineering

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Star sensor is a preferred attitude measurement device for its extremely high accuracy. Star acquisition is the essential and critical procedure, which is aiming at acquiring accurate star areas. However, degenerated acquisition results under complex conditions become one of the major restrictions for modern star sensor. In this paper, an accurate and autonomous star acquisition method is proposed. Mathematical morphology and variable thresholding are combined for accurate star extraction; motion PSF is estimated in frequency domain and nonlinear filter is adopted for star restoration. Accurate star acquisition can be achieved based on only one star image. Simulations and laboratory experiments are conducted for verification. Several existing methods are also reproduced for comparison. Acquisition results demonstrate that the proposed method is effective and an excellent performance can be achieved autonomously under complex conditions, along with more detected stars and improved acquisition accuracy.

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“…Stellar map denoising is the premise of centroid positioning and the results directly determine the postattitude determination of the satellite sensors. [6][7][8] Therefore, effectively mitigating noise and accurate centroid positioning have become one of the important research areas of aerospace remote sensing satellites in recent years. 9,10 Currently, stellar map denoising methods can be roughly divided into two categories: stellar map denoising based on filtering or threshold segmentation method.…”

mentioning

confidence: 99%

“…9,10 Currently, stellar map denoising methods can be roughly divided into two categories: stellar map denoising based on filtering or threshold segmentation method. Stellar maps based on filtering denoising 8,11 mainly include mean filtering, median filtering, Gaussian low-pass filtering, and Wiener filtering. Filtering denoising methods can mitigate the speckle noise in natural images better.…”

mentioning

confidence: 99%

Stellar map centroid positioning based on dark channel denoising and feasibility of jitter detection on ZiYuan3 satellite platform

Zhu

Xie

Tang

et al. 2021

J. Appl. Rem. Sens.

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Stellar map denoising and centroid positioning, which directly determine the postpositioning accuracy of star trackers, are key technologies in stellar map processing. Due to the influence of a complex starry sky background, there is often a large amount of noise in stellar maps, which makes it difficult to accurately locate the stellar centroid. A stellar map processing method based on dark channel denoising and continuous multiframe stellar map centroid positioning combined with centroid trajectory constraints is proposed. First, a dark channel noise template is used for denoising, and the single-point and multipoint noises in the denoising result are filtered. Second, in the process of stellar map positioning, if the maximum gray value of the stellar is not unique, it is constrained by the previous stellar positioning result, an adaptive window is established, and the gray-scale centroid weighting method is used to locate the centroid. Then, the star angular distance is used to analyze the precision of the centroid positioning. Finally, the jitter frequency of the satellite platform based on the continuous multiframe centroid positioning result is used to detect satellite attitude. The experimental results show that the performance of dark channel denoising, which can solve many strip noise and background noise problems in stellar maps, is better than that of the existing stellar map denoising method. The centroid positioning results improves the star angular distance by 18.85 arc sec compared with the Gaussian filter and by 8.03 arc sec compared with the global threshold segmentation method, significantly improving the accuracy of stellar map centroid positioning and laying a foundation for improving the star tracker positioning accuracy. A jitter frequency of 0.67 Hz was detected on the ZiYuan3 satellite platform based on the centroid positioning results. It enriches the jitter detection methods and provides a theoretical and technical basis for the design of future high-resolution ground observation remote sensing satellite platforms and geometric accuracy compensation. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Denoising star map data via sparse representation and dictionary learning

Cited by 6 publications

References 14 publications

Automatic centroid extraction method for noisy star image

Automatic centroid extraction method for noisy star image

Accurate and Autonomous Star Acquisition Method for Star Sensor under Complex Conditions

Stellar map centroid positioning based on dark channel denoising and feasibility of jitter detection on ZiYuan3 satellite platform

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