Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multifocus image fusion using region segmentation and spatial frequency
Image and Vision Computing
Multifocus image fusion by combining curvelet and wavelet transform
Pattern Recognition Letters
Multifocus image fusion using the nonsubsampled contourlet transform
Signal Processing
Wavelet based approach for fusing computed tomography and magnetic resonance images
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Multi-focus image fusion based on salient edge information within adaptive focus-measuring windows
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Noise resilient image fusion based on orthogonal matching pursuit
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multi-focus image fusion based on muti-scheme
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
The direct use of curvelets in multifocus fusion
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Edge preserved image fusion based on multiscale toggle contrast operator
Image and Vision Computing
A regional image fusion based on similarity characteristics
Signal Processing
An efficient algorithm for multi-focus image fusion using PSO-ICA
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Mutual spectral residual approach for multifocus image fusion
Digital Signal Processing
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This paper presents a simple yet efficient algorithm for multifocus image fusion, using a multiresolution signal decomposition scheme. The decomposition scheme is based on a nonlinear wavelet constructed with morphological operations. The analysis operators are constructed by morphological dilation combined with quadratic downsampling and the synthesis operators are constructed by morphological erosion combined with quadratic upsampling. A performance measure based on image gradients is used to evaluate the results. The proposed scheme has some interesting computational advantages as well.