Multiresolution decomposition schemes using the parameterized logarithmic image processing model with application to image fusion

  • Authors:

  • Shahan C. Nercessian;Karen A. Panetta;Sos S. Agaian

  • Affiliations:

  • Department of Electrical and Computer Engineering, Tufts University, Medford, MA;Department of Electrical and Computer Engineering, Tufts University, Medford, MA;Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on theory and application of general linear image processing
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 DiscreteWavelet 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.