Pixel-level image fusion with simultaneous orthogonal matching pursuit

By Bin Yang, Shutao Li. Information Fusion, 2012

364 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/74422933afbfd4d5c599a4fe50baaf45c93ebedd

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. Xun Chen
3. Zengfu Wang
4. Haitao Yin
5. Leyuan Fang
6. Hu Peng
7. Z. J. Wang
8. Rabab Kreidieh Ward

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Yu Liu
3. Hui Li
4. Xun Chen
5. Xiaojun Wu
6. Han Xu
7. Zengfu Wang
8. Hu Peng

In Media Technology

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

Citations in the corpus

• Simultaneous image fusion and denoising with adaptive sparse representation (2015, methodology): “Many improved SR-based fusion methods [21 – 25] have been proposed since this fundamental work appeared.”
• Image Fusion With Convolutional Sparse Representation (2016, methodology): “Since the design of activity level measure and fusion scheme in our methods generally follow the SR-based methods [16], [19], to make a fair and clear comparison between SR and CSR for image fusion (also considering the limitation of paper length), the proposed CSR-based methods are primarily”
• Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, neutral): “Different from standard multi-scale analytic methods, the sparse representation (SR) assumes that both high-frequency and low-frequency components share the same set of sparse coefficients [269] .”
• Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion (2012, methodology): “The dictionaries used in GSLDF and SOMP are learned by DLGSGR and K-SVD offline, respectively.”
• A general framework for image fusion based on multi-scale transform and sparse representation (2015, background): “A typical example is that the dictionary size is 256 when the input signal is 64 dimensional (8 (cid:2) 8 image patch) [15].”
• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “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, 3”
• Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, methodology): “onal matching pursuit (SR-SOMP) method [4], the guided filtering (GF) method [11], the phase congruency and directive contrast in NSCT domain (NSCT-PCDC) method [22], the NSCT-RPCNN method [23], the LP-SR method [8], the CTD-SR method [28], the LLF-IOI method [29], and the LP-CNN method [30].”
• A medical image fusion method based on convolutional neural networks (2017, methodology): “The proposed fusion method is compared with other three recently proposed medical image fusion methods, which are the non-subsampled contourlet transform based method with phase congruency and directive contrast (NSCT-PCDC) [6], the sparse representation based method with simultaneous orthogonal mat”
• An overview of multi-modal medical image fusion (2016, methodology): “Sparse representation methods Different from the traditional MSD methods, sparse representation is proposed as a tool for fusing multi-modal medical images with the assumption that both the high and low frequency images share the same set of sparse coefficients [17, 18].”
• Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “In the SR-based methods, there were new sparse models and more complex fusion rules in [35–37] .”