A novel multi-modality image fusion method based on image decomposition and sparse representation

By Zhiqin Zhu, Hongpeng Yin, Yi Chai, Yanxia Li, Guanqiu Qi. Information Sciences, 2017

366 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/0f172ce9b212ff68bb8d6edaa0e06533b3e0ea4c

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. Xun Chen
2. Hui Li
3. Wei Tang
4. Y. Liu
5. Fazhi He
6. Yu Liu
7. Ming Yin
8. Xiaoning Liu

In Media Technology

1. Hui Li
2. Xun Chen
3. Wei Tang
4. Y. Liu
5. Fazhi He
6. Risheng Liu
7. Jinyuan Liu
8. Tongzhen Si

Citations in the corpus

• DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer (2023, methodology): “…methods [46], independent component analysis (ICA)-based methods [32], etc. Hybrid methods, as the name suggests, are composed of two or more traditional-based methods, such as a combination of MST-based and SR-based methods [31] and a combination of MST-based and subspace-based approaches [47].”
• YDTR: Infrared and Visible Image Fusion via Y-Shape Dynamic Transformer (2023, methodology): “The conventional methods primarily involve multi-scale transform (MST)-based methods [8], [9], [11], sparse representation (SR)-based methods [12], [15], total variation (TV)-based meth-ods [10], [14], hybrid methods [13], [16], and other methods [1], [17].”
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection (2022, background): “Traditional multi-scale transform [10, 24], optimization model [16, 20, 41], spare representation [37, 43], and subspace methods attempt to discover intrinsic common features of the two modalities and to design appropriate weighting rules for fusion.”
• 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].”