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Related Experiment Video

Updated: Oct 22, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Star Sensor Denoising Algorithm Based on Edge Protection.

Kaili Lu^1,2, Enhai Liu^1,2, Rujin Zhao^1,2

¹Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610000, China.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This study introduces an Improved Gaussian Side Window Filtering (IGSWF) algorithm to eliminate single-pixel noise in star sensors. The IGSWF algorithm significantly reduces centroid estimation error, improving star image quality.

Keywords:

denoising reconstruction function single-pixel noise star sensor subtemplates

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Last Updated: Oct 22, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Published on: March 13, 2017

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Image Processing
Astrophysics
Sensor Technology

Background:

Single-pixel noise in star sensors leads to centroid extraction errors.
Accurate star tracking is crucial for spacecraft navigation and astronomical observation.

Purpose of the Study:

To develop an effective star image denoising algorithm for star sensors.
To reduce centroid estimation error caused by single-pixel noise.

Main Methods:

Proposed the Improved Gaussian Side Window Filtering (IGSWF) algorithm.
Utilized four triangular Gaussian subtemplates for edge protection.
Employed a reconstruction function tailored to star and noise characteristics.

Main Results:

IGSWF effectively preserves star shape while eliminating single-pixel noise.
Centroid Estimation Error (CEE) was reduced eightfold compared to original data.
IGSWF outperformed traditional window filtering and side window filtering methods.

Conclusions:

The IGSWF algorithm is a robust solution for denoising star images.
IGSWF significantly improves the accuracy of centroid extraction in star sensors.
This method enhances the reliability of star sensors in space applications.