Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Medical image fusion using m-PCNN
Information Fusion
Contrast enhancement in emission tomography by way of synergistic PET/CT image combination
Computer Methods and Programs in Biomedicine
Multifocus image fusion using the nonsubsampled contourlet transform
Signal Processing
Image fusion based on a new contourlet packet
Information Fusion
MRI and PET image fusion by combining IHS and retina-inspired models
Information Fusion
A filter bank for the directional decomposition of images: theoryand design
IEEE Transactions on Signal Processing
The discrete wavelet transform: wedding the a trous and Mallatalgorithms
IEEE Transactions on Signal Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
Human visual system inspired multi-modal medical image fusion framework
Expert Systems with Applications: An International Journal
Image matting for fusion of multi-focus images in dynamic scenes
Information Fusion
Simultaneous image fusion and super-resolution using sparse representation
Information Fusion
Poisson image fusion based on Markov random field fusion model
Information Fusion
Tunable-Q contourlet-based multi-sensor image fusion
Signal Processing
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Biological image fusion, as a powerful tool for the protein study, has developed with the advent of various imaging modalities in molecular biology. Combining the fluorescent image and its corresponding phase contrast image will benefit the localization of the protein. However, resulting images of traditional methods are always difficult to compromise between multimodalities. This paper has solved this problem by a variable-weight fusion rule based on the nonsubsampled contourlet transform (NSCT). The intensity components of original images are combined in the multiscaled space and the fused image is obtained in the generalized intensity-hue-saturation (GIHS) frame. Validation experiments on 117 sets of Arabidopsis images are for two purposes: the comparison among different fusion rules and the impact of the multiscaled analysis in biological image fusion. Region-based quantified indexes reveal the similarity between fused images and original ones, and therefore demonstrate the superiority of the proposed method over traditional methods.