Pixel- and region-based image fusion with complex wavelets

By J. Lewis, R. O'Callaghan, Stavri G. Nikolov, D. Bull, C. N. Canagarajah. Information Fusion, 2007

829 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/15091cc289ff7aa2bf1794288efff5857be50290

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. Jiayi Ma
2. Han Xu
3. Hui Li
4. Junjun Jiang
5. Yu Liu
6. Hao Zhang
7. Xiaojun Wu
8. Xun Chen

In Media Technology

1. Jiayi Ma
2. Hui Li
3. Han Xu
4. Junjun Jiang
5. Hao Zhang
6. Yu Liu
7. Xiaojun Wu
8. Xun Chen

Citations in the corpus

• Artificial intelligence in the creative industries: a review (2020, neutral): “Existing pixel-level fusion schemes range from simple averaging of the pixel values of registered (aligned) images to more complex multiresolution (MR) pyramids, sparse methods [195] and methods based on complex wavelets [284].”
• A medical image fusion method based on convolutional neural networks (2017, background): “Multi-scale transforms which are frequently studied in image fusion include pyramids [17]–[19], wavelets [9], [20], [21], multi-scale geometrical transforms like contourlet and shearlet”
• Simultaneous image fusion and denoising with adaptive sparse representation (2015, methodology): “…used in TDF methods include morphological pyramid [8], gradient pyramid [9], discrete wavelet transform (DWT) [10], dual tree complex wavelet transform (DTCWT) [11], curvelet transform (CVT) [12], non-subsampled contourlet transform (NSCT) [13], non-subsampled shearlet transform [14] etc.”
• AttentionFGAN: Infrared and Visible Image Fusion Using Attention-Based Generative Adversarial Networks (2021, methodology): “The typical MST based methods include laplacian pyramid transform (LP) [8], wavelet transform (Wavelet) [9], ratio of low pass pyramid (RP) [10], multi-resolution singular value decomposition (MSVD) [11], complex wavelet transform (DTCWT) [12] and curvelet transform (CVT) [13].”
• FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, background): “…datasets, and compare it with eight state-of-the-art fusion methods including adaptive sparse representation (ASR) [37], curvelet transform (CVT) [38], dual-tree complex wavelet transform (DTCWT) [39], fourth order partial differential equation (FPDE) [12], guided filtering based fusion (GFF) A 4.1.”
• A non-reference image fusion metric based on mutual information of image features (2011, methodology): “That is, the minimization of CE is substituted by the maximization of MI [6,13,29].”
• Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “Some representative examples include the morphological pyramid (MP)-based method [3] , the discrete wavelet transform (DWT)-base method [4] , the dual-tree complex wavelet transform (DTCWT)-based method [5] , and the non-subsampled contourlet transform (NSCT)-based method [6] .”
• Pixel-level image fusion with simultaneous orthogonal matching pursuit (2012, background): “5a and b with different fusion methods based on DWT , DTCWT , SWT , CVT , NSCT , and our proposed methods are shown in Figs .”
• Image matting for fusion of multi-focus images in dynamic scenes (2013, methodology): “Despite the fact that most details have been merged by the DTCWT, NSCT, SR and MM based methods, more or less artifacts appear in the motion area such as the head of the girl (see Fig.”
• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “E As a solution to these problems, the dual-tree 90 complex wavelet transform is successfully applied for image fusion[13].”
• GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “From top to bottom: infrared images, visible images, fused results of LPP [44], LP [45], CVT [46], DTCWT [47], GTF [24], CNN [37], FusionGAN [16], and our GANMcC. and adversarial loss.”
• A general framework for image fusion based on multi-scale transform and sparse representation (2015, methodology): “Classical 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 transfo”