International Journal of Computer Vision

, Volume 70, Issue 3, pp 279–298

Image Deblurring in the Presence of Impulsive Noise

  • Leah Bar
  • Nahum Kiryati
  • Nir Sochen
Article

Abstract

Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Median-based approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradient-based edge-preserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L1 norm, and elements from the Mumford-Shah functional are used for regularization. We show that the Mumford-Shah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results.

Keywords

image deblurring restoration impulse noise salt and pepper noise variational methods 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Leah Bar
    • 1
  • Nahum Kiryati
    • 1
  • Nir Sochen
    • 2
  1. 1.School of Electrical EngineeringTel Aviv UniversityTel AvivIsrael
  2. 2.Dept. of Applied MathematicsTel Aviv UniversityTel AvivIsrael

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