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Using a Blur Metric to Estimate Linear Motion Blur Parameters.

Taiebeh Askari Javaran1, Hamid Hassanpour2

  • 1Computer Science Department, Faculty of Mathematics and Computer, Higher Education Complex of Bam, Bam, Iran.

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|November 8, 2021
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Summary
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This study introduces a new method for estimating linear motion blur parameters in images. The technique uses image features to accurately determine blur direction and length, improving image deblurring and medical image enhancement.

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Area of Science:

  • Image Processing
  • Computer Vision
  • Medical Imaging

Background:

  • Motion blur is a prevalent artifact in digital images, particularly impacting e-health services by degrading image quality.
  • Accurate estimation of linear motion blur parameters (length and direction) is crucial for effective image deblurring and enhancement.
  • Blurry medical images can hinder accurate diagnosis, necessitating robust deblurring techniques.

Purpose of the Study:

  • To propose and evaluate novel methods for estimating linear motion blur parameters from single blurred images.
  • To enhance the quality of medical images in e-health applications by enabling precise blur parameter estimation.
  • To develop a reliable approach for image deblurring through accurate motion blur characterization.

Main Methods:

  • Motion blur direction is estimated using the Radon transform applied to the spectrum of the blurred image.
  • Motion blur length is determined by leveraging the monotonic relationship between blur length and a Noise-Immune Discrete Cosine Transform (NIDCT)-based blur metric.
  • Feature extraction from the blurred image is employed for parameter estimation.

Main Results:

  • A monotonic relationship was experimentally confirmed between the NIDCT blur metric and motion blur length.
  • The proposed method effectively estimates both the direction and length of linear motion blur.
  • Quantitative and qualitative experiments demonstrate the efficiency of the developed technique.

Conclusions:

  • The proposed method provides an effective means for estimating linear motion blur parameters.
  • Accurate blur parameter estimation facilitates significant improvements in image deblurring, with potential applications in e-health.
  • The NIDCT metric offers a reliable basis for quantifying blurriness and estimating motion blur length.