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Adaptive regularization for multiple image restoration using an extended total variations approach
Faculty of Informatics - Papers (Archive)
  • Matthew Kitchener, University of Wollongong
  • Abdesselam Bouzerdoum, University of Wollongong
  • Son Lam Phung, University of Wollongong
RIS ID
50705
Publication Date
1-1-2011
Publication Details

Kitchener, M. Andrew., Bouzerdoum, A. & Phung, S. (2011). Adaptive regularization for multiple image restoration using an extended total variations approach. 2011 18th IEEE International Conference on Image Processing, ICIP 2011 (pp. 697-700). USA: IEEE.

Abstract

In this paper a Variational Inequality method for multiple in- put, multiple output image restoration is presented using an extended Total Variations (TV) regularizer. This approach calculates an adaptive regularization parameter for each image based on their respective degradations. The proposed ex- tended Total Variations regularizer combines both intra-image and inter-image pixel information for improved restoration performance. Hyperparameters for controlling this new TV measure are calculated using a Bayesian joint maximum a posteriori approach.

Citation Information
Matthew Kitchener, Abdesselam Bouzerdoum and Son Lam Phung. "Adaptive regularization for multiple image restoration using an extended total variations approach" (2011) p. 697 - 700
Available at: http://works.bepress.com/sbouzerdoum/21/