Open Access

Multiresolution Decomposition Schemes Using the Parameterized Logarithmic Image Processing Model with Application to Image Fusion

  • Shahan C. Nercessian1Email author,
  • Karen A. Panetta1 and
  • Sos S. Agaian2
EURASIP Journal on Advances in Signal Processing20102011:515084

DOI: 10.1155/2011/515084

Received: 23 June 2010

Accepted: 7 October 2010

Published: 11 October 2010


New pixel- and region-based multiresolution image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing images. A mathematical analysis shows that the Logarithmic Image Processing (LIP) model and standard mathematical operators are extreme cases of the PLIP model operators. Moreover, the PLIP model operators also have the ability to take on cases in between LIP and standard operators based on the visual requirements of the input images. PLIP-based multiresolution decomposition schemes are developed and thoroughly applied for image fusion as analysis and synthesis methods. The new decomposition schemes and fusion rules yield novel image fusion algorithms which are able to provide visually more pleasing fusion results. LIP-based multiresolution image fusion approaches are consequently formulated due to the generalized nature of the PLIP model. Computer simulations illustrate that the proposed image fusion algorithms using the Parameterized Logarithmic Laplacian Pyramid, Parameterized Logarithmic Discrete Wavelet Transform, and Parameterized Logarithmic Stationary Wavelet Transform outperform their respective traditional approaches by both qualitative and quantitative means. The algorithms were tested over a range of different image classes, including out-of-focus, medical, surveillance, and remote sensing images.

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Authors’ Affiliations

Department of Electrical and Computer Engineering, Tufts University
Department of Electrical and Computer Engineering, University of Texas at San Antonio


© Shahan C. Nercessian et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.