Star Sensor Denoising Algorithm Based on Edge Protection

Lu, Kaili; Liu, Enhai; Zhao, Rujin; Hui, Zhang; Tian, Hong

doi:10.3390/s21165255

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Several denoising techniques have been developed recently to pre-filter the star images to preserve their shapes. Methodologies to ward off the interference of the background and enhance the star image-background contrast include the following: block adaptive threshold segmentation (BATS) [ 26 ], ring filter [ 27 ], hybrid filter [ 28 ], dark channel filter [ 29 ], and Improved Gaussian Side Window Filter (IGSWF) [ 30 ]. In the case of star sensors with small field-of-view (FOV), even muddled star image spots must be included in the attitude estimation process.…”

Section: Introductionmentioning

confidence: 99%

Sieve Search Centroiding Algorithm for Star Sensors

Karaparambil

Manjarekar

Singru

2023

Sensors

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The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.

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Section: Introductionmentioning

confidence: 99%

Sieve Search Centroiding Algorithm for Star Sensors

Karaparambil

Manjarekar

Singru

2023

Sensors

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“…Chen et al employed SWF for image-dehazing optimization, ensuring that texture and edge information was preserved [ 21 ]. Lu et al suggested an improved SWF algorithm for edge-preserving denoising filtering of star maps collected by in-orbit star-sensitive sensors to improve the star point localization accuracy [ 22 ]. The above-mentioned related studies revealed that SWF has both morphological and grayscale statistical properties, so it was used as an operator for morphological clustering of local pixels, allowing the hereby proposed binarization algorithm to consider both local grayscale distribution information and morphological information.…”

Section: Introductionmentioning

confidence: 99%

Binarization Algorithm Based on Side Window Multidimensional Convolution Classification

Ren

Wang

Dong

2022

Sensors

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Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.

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