Simultaneous Denoising and Illumination Correction via Local Data-Fidelity and Nonlocal Regularization

  • Jun Liu
  • Xue-cheng Tai
  • Haiyang Huang
  • Zhongdan Huan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)


In this paper, we provide a new model for simultaneous denoising and illumination correction. A variational framework based on local maximum likelihood estimation (MLE) and a nonlocal regularization is proposed and studied. The proposed minimization problem can be efficiently solved by the augmented Lagrangian method coupled with a maximum expectation step. Experimental results show that our model can provide more homogeneous denoisng results compared to some earlier variational method. In addition, the new method also produces good results under both Gaussian and non-Gaussian noise such as Gaussian mixture, impulse noise and their mixtures.


Noisy Image Impulse Noise Augmented Lagrangian Method Bias Function Split Bregman Method 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jun Liu
    • 1
  • Xue-cheng Tai
    • 2
    • 3
  • Haiyang Huang
    • 1
  • Zhongdan Huan
    • 1
  1. 1.School of Mathematical Sciences, Laboratory of Mathematics and Complex SystemsBeijing Normal UniversityBeijingP.R. China
  2. 2.School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore
  3. 3.Department of MathematicsUniversity of BergenNorway

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