Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Image selective smoothing and edge detection by nonlinear diffusion. II
SIAM Journal on Numerical Analysis
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Multifocus image fusion using artificial neural networks
Pattern Recognition Letters
A variational approach to image fusion
A variational approach to image fusion
Color transfer based remote sensing image fusion using non-separable wavelet frame transform
Pattern Recognition Letters
A Variational Model for P+XS Image Fusion
International Journal of Computer Vision
Multi-focus image fusion using pulse coupled neural network
Pattern Recognition Letters
Fusing remote sensing images using à trous wavelet transform and empirical mode decomposition
Pattern Recognition Letters
Multifocus image fusion by combining curvelet and wavelet transform
Pattern Recognition Letters
Image Fusion: Algorithms and Applications
Image Fusion: Algorithms and Applications
Image Fusion for Enhanced Visualization: A Variational Approach
International Journal of Computer Vision
Multiframe selective information fusion from robust error estimation theory
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
Multi-focus thermal image fusion
Pattern Recognition Letters
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In this letter we propose a variational approach for concurrent image fusion and denoising of multifocus images, based on error estimation theory and Partial Differential Equations (PDEs). In real world scenarios the assumption that the inputs of an image fusion process contain only useful information, pertinent to the desired fused output, does not hold true more often than not. Thus, the image fusion problem needs to be addressed from a more complex, fusion-denoising point of view, in order to provide a fused result of greater quality. The novelty of our approach consists in defining an image geometry-driven, anisotropic fusion model, numerically expressed using an anisotropy-reinforcing discretization scheme that further increases the anisotropic behavior of the proposed fusion paradigm. The preliminary experimental analysis shows that robust anisotropic denoising can be attained in parallel with efficient image fusion, thus bringing two paramount image processing tasks into complete synergy. One immediate application of the proposed method is fusion of multifocus, noise-corrupted images.