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Blurred image restoration using knife-edge function and optimal window Wiener filtering.

Min Wang1, Shudao Zhou1,2, Wei Yan1

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This study introduces a novel image restoration method using a knife-edge function and optimal window Wiener filtering to effectively remove motion blur. The technique enhances image resolution and contrast without introducing ringing artifacts.

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Area of Science:

  • Digital Image Processing
  • Signal Processing

Background:

  • Motion blur significantly degrades image quality, often modeled as a convolution with a point spread function (PSF).
  • Traditional methods require prior knowledge of the blur model, limiting their applicability.

Purpose of the Study:

  • To develop an effective image restoration technique for motion-blurred images.
  • To utilize the knife-edge function for accurate system degradation estimation.
  • To implement optimal window Wiener filtering for blur removal.

Main Methods:

  • Calculating motion blur parameters and constructing an optimal window.
  • Employing the knife-edge function to determine the system degradation function.
  • Applying Wiener filtering for image restoration.

Main Results:

  • The proposed method successfully restores motion-blurred images.
  • Restored images exhibit improved resolution and contrast.
  • The technique effectively eliminates ringing artifacts.

Conclusions:

  • The knife-edge function combined with optimal window Wiener filtering provides a robust solution for motion blur removal.
  • This approach offers enhanced image quality with clear details and no visual distortions.