A new total variation model for restoring blurred and speckle noisy images

Jian Lu, Yupeng Chen, Yuru Zou, Lixin Shen

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

In coherent imaging systems, such as the synthetic aperture radar (SAR), the observed images are affected by multiplicative speckle noise. This paper proposes a new variational model based on I-divergence for restoring blurred images with speckle noise. The model minimizes the sum of an I-divergence data fidelity term, a new quadratic penalty term based on the statistical property of the noise and the total-variation regularization term. The existence and uniqueness of a solution of the proposed model with some other characteristics are analyzed. Furthermore, an iterative algorithm is introduced to solve the proposed variational model. Our numerical experiments indicate that the proposed method performs favorably.

Keywords

  • deblurring
  • I-divergence
  • Speckle noise
  • total variation

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Applied Mathematics

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