Evaluation of Image Fusion Performance with Visible Differences

  • Vladimir Petrović
  • Costas Xydeas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)


Multisensor signal-level image fusion has attracted considerable research attention recently. Whereas it is relatively straightforward to obtain a fused image, e.g. a simple but crude method is to average the input signals, assessing the performance of fusion algorithms is much harder in practice. This is particularly true in widespread “fusion for display” applications where multisensor images are fused and the resulting image is presented to a human operator. As recent studies have shown, the most direct and reliable image fusion evaluation method, subjective tests with a representative sample of potential users are expensive in terms of both time/effort and equipment required. This paper presents an investigation into the application of the Visible signal Differences Prediction modelling, to the objective evaluation of the performance of fusion algorithms. Thus given a pair of input images and a resulting fused image, the Visual Difference Prediction process evaluates the probability that a signal difference between each of the inputs and the fused image can be detected by the human visual system. The resulting probability maps are used to form objective fusion performance metrics and are also integrated with more complex fusion performance measures. Experimental results indicate that the inclusion of visible differences information in fusion assessment yields metrics whose accuracy, with reference to subjective results, is superior to that obtained from the state of the art objective fusion performance measures.


Input Image Image Fusion Fusion Evaluation Visible Difference Fusion Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Vladimir Petrović
    • 1
  • Costas Xydeas
    • 2
  1. 1.Imaging Science Biomedical EngineeringUniversity of ManchesterManchesterUK
  2. 2.Dept. Communication SystemsUniversity of LancasterLancasterUK

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