Lapped transform-based image denoising with the generalised Gaussian prior

  • Authors:
  • Vijay Kumar Nath;Deepika Hazarika;Anil Mahanta

  • Affiliations:
  • Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam, 784028, India;Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam, 784028, India;Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

  • Venue:
  • International Journal of Computational Vision and Robotics
  • Year:
  • 2014

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Abstract

We introduce a new image denoising method based on the statistical modelling of dyadic rearranged lapped transform LT coefficients. Based on Kolomogrov-Smirnov KS goodness of fit test, we have shown that the statistical distribution of the dyadic rearranged LT coefficients in a subband is best approximated by the generalised Gaussian distribution. A Bayesian minimum mean square error MMSE estimator is used to obtain the estimate of noise free coefficients, which is based on modelling the global distribution of the dyadic rearranged LT coefficients using generalised Gaussian distribution. The LT-based image denoising method with generalised Gaussian prior shows highly encouraging both objective and subjective results when compared to several well-known image denoising methods.