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07:05
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Published on: June 18, 2021
A Vignetting Correction Method for Remote Sensing Images Based on Low-Rank Modeling and Polynomial Fitting
Xue Zhao1,2, Zhuoyue Hu1, Zhengqin Xu1
1State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
Journal of Imaging
|July 27, 2026
Summary
This study introduces a novel vignetting correction method for remote sensing images using low-rank modeling and polynomial fitting. The technique effectively reduces radiometric nonuniformity, enhancing image analysis and calibration accuracy.
Area of Science:
- Remote Sensing
- Image Processing
- Photogrammetry
Background:
- Vignetting causes spatial radiometric nonuniformity in remote sensing images.
- This nonuniformity degrades radiometric analysis, image interpretation, and calibration.
Purpose of the Study:
- To develop and validate a robust vignetting correction method for remote sensing images.
- To improve radiometric accuracy and stability in image analysis and calibration.
Main Methods:
- Constructing a multi-frame data matrix in the logarithmic domain.
- Extracting the shared vignette component using rank-1 low-rank modeling.
- Recovering a smooth vignette field via polynomial fitting.
Main Results:
- The proposed method achieved superior performance across real, simulated, and star images.
- Significant reductions in Mean Absolute Error (MAE) and Mean Absolute Deviation (MAD) were observed.
- The rank-1 model demonstrated accuracy comparable to standard RPCA with lower variability.
Conclusions:
- The method provides physically interpretable and stable vignetting correction.
- Radiometric consistency is maintained, preserving critical image data.
- The approach enhances the reliability of remote sensing data for various applications.
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