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Total variation blind deconvolution.

T F Chan1, C K Wong

  • 1Dept. of Math., California Univ., Los Angeles, CA 90095-1555, USA. chan@math.ucla.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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This study introduces a blind deconvolution algorithm using total variational (TV) minimization. The method effectively recovers images and point spread functions (PSFs), even with high noise levels and various blur types.

Area of Science:

  • Image processing
  • Computer vision
  • Applied mathematics

Background:

  • Blind deconvolution is crucial for image restoration.
  • Traditional methods struggle with complex blurs and noise.
  • Total Variational (TV) norm regularization shows promise for edge preservation.

Purpose of the Study:

  • To develop a robust blind deconvolution algorithm.
  • To simultaneously recover images and their blurring functions (PSFs).
  • To leverage TV minimization for enhanced image and PSF recovery.

Main Methods:

  • Utilizing a total variational (TV) minimization approach for regularization.
  • Employing an alternating minimization (AM) implicit iterative scheme.
  • Applying the method to various blur types, including motion, out-of-focus, and Gaussian blur.

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Main Results:

  • The algorithm demonstrates robustness and fast convergence, particularly for discontinuous blurs.
  • Successful recovery of both images and point spread functions (PSFs) under high noise conditions.
  • Effectiveness shown for blurs with and without sharp edges, like Gaussian blur.

Conclusions:

  • TV minimization is a powerful tool for blind deconvolution.
  • The proposed AM-implicit iterative scheme offers a reliable solution for image and PSF recovery.
  • The algorithm is effective across a range of blurring scenarios and noise levels.