Machine Vision and Applications
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Gradient-based multiresolution image fusion
IEEE Transactions on Image Processing
Objectively adaptive image fusion
Information Fusion
Image Fusion for Enhanced Visualization: A Variational Approach
International Journal of Computer Vision
Evaluating rank-order code performance using a biologically-derived retinal model
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Biologically inspired means for rank-order encoding images: a quantitative analysis
IEEE Transactions on Neural Networks
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
A new image fusion performance metric based on visual information fidelity
Information Fusion
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This paper focuses on the methodology for perceptual image fusion assessment through comparative tests and validation of objective fusion evaluation metrics. Initially, the theory of subjective fusion evaluation, adopted practice and methods to gauge relevance and significance of individual trials are examined. Further in this context, the methodology, experiences and results of a series of specific, subjective preference tests aimed at relative evaluation of fusion algorithms are presented. Test conditions and experimental procedure are described in detail and a number of explicit fusion metrics derived from the subjective test data are proposed. Relative fusion quality, fusion performance robustness (to content) and personal preference are all assessed by the metrics as different aspects of general image fusion performance. Finally, the methodology for subjective validation of objective fusion metrics using the reported test procedures is presented. In particular, explicit subjective-objective validation algorithms are defined and applied to a range of established objective measures of fusion performance in order to evaluate their subjective relevance.