A general framework for image fusion based on multi-scale transform and sparse representation

By Yu Liu, Shuping Liu, Zengfu Wang. Information Fusion, 2015

1,343 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/399286581a9a1c7adc471068b8d49098bce9846c

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. Hui Li
3. Han Xu
4. Yiannis Demiris
5. Xingchen Zhang
6. Hao Zhang
7. Xiaojun Wu
8. Junjun Jiang

In Media Technology

1. Jiayi Ma
2. Hui Li
3. Yiannis Demiris
4. Xingchen Zhang
5. Han Xu
6. Hao Zhang
7. Xiaojun Wu
8. Junjun Jiang

Citations in the corpus

• Pixel-level image fusion: A survey of the state of the art (2017, methodology): “…[88], IHS-wavelet[89] coefficient and window based activity level measurement[85–87], choose-max and weighted-average based coefficient combining method[85–87], C This image fusion framework involves two basic problems, i.e., the choice of the multi-scale decomposition method, and the adopted fusion…”
• FusionGAN: A generative adversarial network for infrared and visible image fusion (2019, methodology): “They can be simply divided into seven categories including multi-scale transform- [5, 6, 7], sparse representation- [8, 9], neural network- [10, 11], subspace- [12, 13], and saliency-based [14, 15] methods, hybrid models [16, 17], and other methods [18, 19].”
• Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, neutral): “…real world, numerous sources of medical data, including Magnetic Resonance Imaging (MRI), X-ray, Positron Emission Tomography (PET), Computerized Tomography scan (CT scan), have provided doctors vast volumes of information (Liu, Liu, & Wang, 2015; Rehman, Zia, et al., 2020; Shen, Wu, & Suk, 2017).”
• A medical image fusion method based on convolutional neural networks (2017, background): “To improve the fusion performance, many complex decomposition approaches and elaborate weight assignment strategies have been recently proposed in the literature [6], [8]–[13], [15], [16].”
• FusionDN: A Unified Densely Connected Network for Image Fusion (2020, background): “…(Liu and Wang 2015), GBM (Paul, Sevcenco, and Agathoklis 2016), Deepfuse (Prab-hakar, Srikar, and Babu 2017) and FLER (Yang et al. 2018) for multi-exposure fusion, MSTSR (Liu, Liu, and Wang 2015a), DSIFT (Liu, Liu, and Wang 2015b), GBM, CNN (Liu et al. 2017b) and DenseFuse for multi-focus fusion.”
• Visible and Infrared Image Fusion Using Deep Learning (2023, background): “active research topic for many years [1], [2], [3], [4], [5], [6].”
• SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, methodology): “In the past decades, numerous image fusion techniques have been proposed, which can be broadly divided into two categories, i.e. , task-specific image fusion schemes [11]–[14] and general image fusion algorithms [10], [15], [16].”
• 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].”