SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion
By Hao Zhang, Jiayi Ma. International Journal of Computer Vision, 2021
669 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/1e6f7e7fd14ec0ab6db340be561332333bf3fd46
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
In Media Technology
- Radu Timofte
- Luc Van Gool
- Kai Zhang
- Yulun Zhang
- Yiannis Demiris
- Xingchen Zhang
- Jiayi Ma
- Linfeng Tang
Citations in the corpus
- SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness (2022, background): “They first manually assigned appropriate weights for sub-losses according to the characteristics of different fusion missions [40], and further developed an adaptive decision block to dynamically assign weights for sub-loss terms to cope with the complex scenario [41].”
- SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, methodology): “On the basis of PMGI, Zhang et al. proposed a squeeze-and-decomposition network and an adaptive decision block to further improve the fusion performance [60].”
- Visible and Infrared Image Fusion Using Deep Learning (2023, methodology): “For example, Zhanget al. [93] utilizedasqueeze-and-decomposition network and manually adjusted weights in the intensity loss to satisfy the requirements on intensity distribution in different image fusion tasks.”
- CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion (2022, methodology): “In this section, we test CDDFuse on the three test sets and compare the fusion results with the state-of-the-art meth-ods including DIDFuse [88], U2Fusion [70], SDNet [82], RFNet [72], TarDAL [35], DeFusion [32] and ReCoNet [19].”
- DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion (2023, methodology): “For discriminative methods, auto encoder-based models [72, 23, 21, 28, 53, 22, 65] use encoders and decoders to extract features and fuse them on a high-dimensional manifold.”