Machine Vision and Applications
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Sequence Fusion Using a Shift-Invariant Wavelet Transform
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
ACM SIGGRAPH 2003 Papers
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image fusion for context enhancement and video surrealism
Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering
Interactive digital photomontage
ACM SIGGRAPH 2004 Papers
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
ACM SIGGRAPH 2006 Papers
Remote Sensing Image Fusion on Gradient Field
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Remote sensing image fusion using the curvelet transform
Information Fusion
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
A total variation-based algorithm for pixel-level image fusion
IEEE Transactions on Image Processing
Image fusion based on a new contourlet packet
Information Fusion
GradientShop: A gradient-domain optimization framework for image and video filtering
ACM Transactions on Graphics (TOG)
Biological image fusion using a NSCT based variable-weight method
Information Fusion
Gradient-based multiresolution image fusion
IEEE Transactions on Image Processing
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In this paper, we present a gradient domain image fusion framework based on the Markov Random Field (MRF) fusion model. In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients. To fuse the structures in the gradient domain, an effective MRF-based fusion model is designed based on both the per-pixel fusion rule defined by the local saliency and also the smoothness constraints over the fusion weights, which is optimized by graph cut algorithm. This MRF-based fusion model enables the accurate estimation of region-based fusion weights for the salient objects or structures. We apply this method to the applications of multi-sensor image fusion, including infrared and visible image fusion, multi-focus image fusion and medical image fusion. Extensive experiments and comparisons show that the proposed fusion model is able to better fuse the multi-sensor images and produces high-quality fusion results compared with the other state-of-the-art methods.