Accuracy enhancement of navigation images using blind restoration method

Zhao, Jianhui; Zhang, Cundu; Yu, Tao; Li, Fan

doi:10.1016/j.actaastro.2017.10.037

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…A method of star image under dynamic conditions which is effective in the motion-blurred star image processing is discussed in [28] and [29], and it is adaptive to different dynamic conditions. Zhao et al [30] propose an improved median filtering and fast blind restoration method, which can effectively remove noise and restore the star image, but the energy of star points is not particularly concentrated. However, ideal navigation images are different from natural images, since most pixels of the navigation images are zero and only a small amount of pixels contains beacon or star spot information [25].…”

Section: Related Workmentioning

confidence: 99%

Blind Deblurring Using Space Target Features

et al. 2019

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The star image suffers inevitably from degradation due to the high-speed motion of the space target and the long exposure time of the camera, therefore the attitude information of the star is hard to accurately obtain. This paper proposes a blind deblurring algorithm that combines shape features of space targets. First, the astronomical image is preprocessed using saliency detection. Then, considering the shape characteristics of space targets, the Minimum Bounding Rectangle (MBR) is introduced to describe the space targets. Next, the parameters of the MBR are used to estimate the point spread function (PSF). Finally, a regularization method is employed to recover the astronomical images. Experimental results on both simulation and real star images demonstrate that the proposed method reduces the error of point spread function estimation and decreases the error rate of identified space targets. At the same time, the accuracy of the star centroid extraction improves 0.2786 on average and the error of the star centroid location extraction reduces 0.059 comparing to the state-of-art method. The proposed method is of great significance for positioning, recognition and attitude determination of space targets. INDEX TERMS Blind deblurring, minimum bounding rectangle, point spread function, star image restoration.

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Section: Related Workmentioning

confidence: 99%

Blind Deblurring Using Space Target Features

et al. 2019

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“…Ma et al [14] adopted an improved algorithm which is based on one-dimensional Wiener filter to improve accuracy. Zhao et al [15] proposed an improved median filtering and blind restoration method. Krishnan and Fergus [16] proposed to use hyper-Laplacian priors for image restoration.…”

Section: Introductionmentioning

confidence: 99%

A blurred star image restoration method based on gyroscope data and enhanced sparse model

Zhao

et al. 2023

Meas. Sci. Technol.

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Star sensors usually have a fixed exposure time to guarantee detection of adequate navigation stars. In a high dynamic environment, star images suffer from degradation due to spacecraft movement, which will severely affects both centroid extraction and attitude accuracy. This paper presents an algorithm for the restoration of motion-blurred star images. The algorithm employs gyroscope assistance and consists of two steps: preprocessing and motion-blurred image restoration. In the preprocessing step, the angular velocity of the gyroscope predicts the motion trajectory, position, and shape of each star point during exposure. This step ensures a good initial estimate of the blur kernel for image restoration. The image restoration step employs an enhanced sparse model inspired by blind deblurring method to solve blur kernel and latent image alternately. Simulations and experiments have verified the effectiveness of the proposed algorithm.

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“…The Radon transform and RL method are combined to estimate the motion kernel, which does not consider complex motion states [18]. The optimization method is adopted for blind restoration [19,20], but it takes a long time. For faster recovery, phase information of smearing is used for Wiener filtering [21], but the noise is not considered in the model.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Identification Algorithm for Smearing Star Image

Han

Yang

et al. 2021

Remote Sensing

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In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

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Accuracy enhancement of navigation images using blind restoration method

Cited by 8 publications

References 10 publications

Blind Deblurring Using Space Target Features

Blind Deblurring Using Space Target Features

A blurred star image restoration method based on gyroscope data and enhanced sparse model

An End-to-End Identification Algorithm for Smearing Star Image

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