Gradient-based multiresolution image fusion

By Vladimir S. Petrovic, Costas S. Xydeas. IEEE Transactions on Image Processing, 2004

570 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/8eed5e2a38f786ab6b2923522ceab4c032e7fea9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Yu Liu
2. Zengfu Wang
3. Shutao Li
4. James W. Davis
5. Vinay Sharma
6. Gaurav Bhatnagar
7. Shuping Liu
8. Bin Yang

In Biomedical Engineering

1. Yu Liu
2. Zengfu Wang
3. Jianwen Hu
4. Shuping Liu
5. Shutao Li
6. Bin Yang
7. James W. Davis
8. Vinay Sharma

In Media Technology

1. Yu Liu
2. Zengfu Wang
3. Xun Chen
4. Jiayi Ma
5. Jianwen Hu
6. Shutao Li
7. Shuping Liu
8. Bin Yang

Citations in the corpus

• Multifocus Image Fusion and Restoration With Sparse Representation (2010, background): “The other category is the transformed domain-based methods, which fuse images with certain frequency or time–frequency transforms [1], [4], [5].”
• Image fusion: Advances in the state of the art (2007, unknown): “Petrović and Xydeas [24] used intensity gradients as a saliency measure.”
• Performance comparison of different multi-resolution transforms for image fusion (2011, background): “The pixel-level fusion integrates visual information contained in source images into a single fused image based on the original pixel information [2].”
• Background-subtraction using contour-based fusion of thermal and visible imagery (2007, methodology): “Some of these techniques include directly combining gradients [40], determining gradients in high dimensions [44], and analyzing gradients at multiple resolutions [43,37].”
• Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “The S-tensor components can only take the following values: À1=2 6 S xx ; S yy ; S zz 6 1 À3=4 6 S xy ; S xz ; S yz 6 3=4 ð3Þ For the common situation in nematic liquid crystals for which the director lies along the magnetic field direction we can express the direct dipole–dipole interaction as: D…”
• A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “…MST-based fusion methods include pyra-mid-based ones like Laplacian pyramid (LP) [2], ratio of low-pass pyramid (RP) [3] and gradient pyramid (GP) [4], wavelet-based ones like discrete wavelet transform (DWT) [5], stationary wavelet transform (SWT) [6] and dual-tree complex wavelet transform…”
• Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain (2013, methodology): “The multimodal medical image fusion results of different fusion algorithms: Fused images from (a1), (a2), (a3), (a4) PCA based technique; (b1), (b2), (b3), (b4) Contrast Pyramid based technique; (c1), (c2), (c3), (c4) Gradient Pyramid based technique; (d1), (d2), (d3), (d4) Wavelet based technique; ”
• Pixel-level image fusion with simultaneous orthogonal matching pursuit (2012, background): “Then, what is the optimal representation of the underlying information of source images? It is obvious that extracting the underlying information of the original images effectively and completely would make the fused image more accurate [15].”