PIAFusion: A progressive infrared and visible image fusion network based on illumination aware

By Linfeng Tang, Jiteng Yuan, Haotong Zhang, Xingyu Jiang, Jiayi Ma. Information Fusion, 2022

1,011 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/9f69d1c839b382a3f04682cbcfbf2d73817bd12a

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. Radu Timofte
2. Luc Van Gool
3. Kai Zhang
4. Yulun Zhang
5. Jiayi Ma
6. Lei Zhang
7. Ming-Hsuan Yang
8. Chen Change Loy

In Media Technology

1. Radu Timofte
2. Luc Van Gool
3. Kai Zhang
4. Yulun Zhang
5. Yiannis Demiris
6. Xingchen Zhang
7. Jiayi Ma
8. Linfeng Tang

Citations in the corpus

• Visible and Infrared Image Fusion Using Deep Learning (2023, positive): “[110]proposedanillumination-awaremoduletoevaluatetheilluminationconditions,which werethenusedtoguidetheimagefusionprocessviaan illumination-awareloss.”
• DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion (2023, methodology): “, TNO [41], RoadScene [46], MSRS [40], and M(3)FD [20].”
• SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, background): “The MSRS dataset [76], [77] The reasons why we set different test set sizes for different image fusion scenarios are as follows.”
• DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer (2023, methodology): “Specifically, 361 pairs of infrared and RGB-visible images are downloaded from the MSRS dataset [80] 29], AUIF [1], DenseFuse [53], FusionGAN [6], GANMcC [55], RFN-Nest [54], CSF [56], MFEIF [24], PPTFusion [67], SwinFuse [7], and the proposed method.”
• CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion (2022, methodology): “IVF experiments use three popular benchmarks to verify our fusion model, i.e., MSRS [57], RoadScene [71], and TNO [59].”
• SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness (2022, methodology): “2) Experimental Configurations: Registration performance of our SuperFusion is compared with five state-of-the-art methods including DASC [62], RIFT [63], GLU-Net [76], UMF-CMGR [30], and CrossRAFT [66] on the MSRS [37] and RoadScene [43] datasets.”