A wavelet-based image fusion tutorial

By Gonzalo Pajares, Jesús Manuel de la Cruz. Pattern Recognition, 2004

1,273 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/073f9559952003734b7b8c78728564d9d0674858

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Hui Li
2. Jiayi Ma
3. Xiaojun Wu
4. Josef Kittler
5. Xiao-Jun Wu
6. Hao Zhang
7. Han Xu
8. Y. Liu

In Media Technology

1. Hui Li
2. Jiayi Ma
3. Xiaojun Wu
4. Josef Kittler
5. Xiao-Jun Wu
6. Hao Zhang
7. Wei Tang
8. Y. Liu

Citations in the corpus

• Multifocus Image Fusion and Restoration With Sparse Representation (2010, methodology): “Through image fusion, extended or enhanced information content can be obtained in the composite image, which has many application fields, such as digital imaging, medical imaging, remote sensing, and machine vision [1]–[5].”
• RFN-Nest: An end-to-end residual fusion network for infrared and visible images (2021, methodology): “In the traditional algorithm category, multi-scale transform methods [8][9][10][11] are widely applied to extract multi-scale features from the source images.”
• DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer (2023, methodology): “Popular MST-based algorithms include pyramid-based algorithms [36], wavelet-based algorithms [37], curvelet-based algorithms [38], multiscale geometric representation-based algorithms [39], and their revised versions.”
• GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “Typical traditional methods are sparse representation-based methods [7], [8], multiscale transform-based methods [9], [10], subspace-based methods [11], saliency-based method [12], [13], and hybrid methods [14], [15].”
• Assessment of image fusion procedures using entropy, image quality, and multispectral classification (2008, methodology): “The inverse wavelet transformation is then performed using the wavelet coefficients and planes from both the multispectral and panchromatic bands [37].”
• Pixel-level image fusion: A survey of the state of the art (2017, background): “I Multi-scale decomposition pyramid[4], wavelet [5, 11, 12], complex wavelet [13], curvelet[14, 15], contourlet [16–23], shear-let [24–26], edge-preserving decompositions[27– 29], anisotropic heat diffusion[30], log-Gabor transform[31], support value transform [32, 33], optimal scale transform [34,…”
• Performance comparison of different multi-resolution transforms for image fusion (2011, background): “In addition, notice that the general image fusion framework with pyramid decomposition and wavelet has been well studied [7,8].”
• Annotated Image Datasets of Rosette Plants (2014, background): “to have the possibility to fuse them in a single image that is in focus everywhere [9], or to enable 3D surface estimation using depth-from-defocus techniques [10].”
• Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion (2012, positive): “The popular fusion methods are based on multiresolution analysis, such as the discrete wavelet transform [9] and stationary wavelet transform [10].”