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Updated: May 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

Automatic Tuning of Gaussian Filter for Image Vignetting Correction.

Artur Bal1,2, Henryk Palus1

  • 1Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16 Str., 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces a new method, Gaussian filter with auto-tuned sigma (GFATS), for correcting image vignetting. GFATS automatically optimizes filter parameters for improved brightness uniformity and accurate vignetting estimation in images.

Keywords:
Gaussian filterautomated filter tuningflat-field correctionimage vignettinglens–camera systemslow-level image processingpolynomial modellingvignetting correction

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Area of Science:

  • Optics and Image Processing
  • Computational Imaging

Background:

  • Image vignetting is a common optical artifact reducing image quality and radiometric accuracy.
  • Existing vignetting correction methods often use Gaussian filtering but struggle with subjective parameter selection.

Purpose of the Study:

  • To develop an automated method for vignetting correction.
  • To improve the accuracy and reliability of vignetting estimation and image brightness uniformity.

Main Methods:

  • Proposed the Gaussian filter with auto-tuned sigma (GFATS) method.
  • Utilized an optimization framework to automatically tune Gaussian filter parameters by aligning filtered output with a polynomial model of vignetting.
  • Derived vignetting correction matrix using the tuned filter.

Main Results:

  • GFATS provides more accurate vignetting estimation and improved brightness uniformity compared to existing methods.
  • The method effectively mitigates overfitting issues common in standard smoothing techniques.
  • A single, consistent parameter set demonstrated reliable performance across diverse lens-camera systems.

Conclusions:

  • GFATS offers a versatile and practical solution for computational image vignetting correction.
  • The automated parameter tuning enhances accuracy and robustness in various imaging applications.
  • The proposed method represents a significant advancement over traditional vignetting correction techniques.