Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Image Processing
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Image fusion using steerable dyadic wavelet transform
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Pixel- and region-based image fusion with complex wavelets
Information Fusion
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
Multi-focus Image Fusion Based on Fuzzy and Wavelet Transform
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Gradient-based multiresolution image fusion
IEEE Transactions on Image Processing
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This paper presents a new wavelet-based algorithm for the fusion of spatially registered infrared and visible images. Wavelet-based image fusion is the most common fusion method, which fuses the information from the source images in the wavelet transform domain according to some fusion rules. We specifically propose new fusion rules for fusion of low and high frequency wavelet coefficients of the source images in the second step of the wavelet-based image fusion algorithm. First, the source images are decomposed using dual-tree discrete wavelet transform (DT-DWT). Then, a fuzzy-based approach is used to fuse high frequency wavelet coefficients of the IR and visible images. Particularly, fuzzy logic is used to integrate the outputs of three different fusion rules (weighted averaging, selection using pixel-based decision map (PDM), and selection using region-based decision map (RDM)), based on a dissimilarity measure of the source images. The objective is to utilize the advantages of previous pixel- and region-based methods in a single scheme. The PDM is obtained based on local activity measurement in the DT-DWT domain of the source images. A new segmentation-based algorithm is also proposed to generate the RDM using the PDM. In addition, a new optimization-based approach using population-based optimization is proposed for the low frequency fusion rule instead of simple averaging. After fusing low and high frequency wavelet coefficients of the source images, the final fused image is obtained using the inverse DT-DWT. This new method provides improved subjective and objectives results as compared to previous image fusion methods.