Infrared and Visible Image Fusion via Decoupling Network
By Xue Wang, Zheng Guan, Shishuang Yu, Jinde Cao, Ya Li. IEEE Transactions on Instrumentation and Measurement, 2022
677 citations recorded by the source corpus.
In general, the goal of the existing infrared and visible image fusion (IVIF) methods is to make the fused image contain both the high-contrast regions of the infrared image and the texture details of the visible image. However, this definition would lead the fusion image losing information from the visible image in high-contrast areas. For this problem, this article proposed a decoupling network-based IVIF method (DNFusion), which utilizes the decoupled maps to design additional constraints on the network to force the network to retain the saliency information of the source image effectively. The current definition of image fusion is satisfied while effectively maintaining the saliency objective of the source images. Specifically, the feature interaction module (FIM) inside effectively facilitates the information exchange within the encoder and improves the utilization of complementary information. Also, a hybrid loss function constructed with weight fidelity loss, gradient loss, and decoupling loss ensures the fusion image to be generated to effectively preserve the source image’s texture details and luminance information. The qualitative and quantitative comparison of extensive experiments demonstrates that our model can generate a fused image containing saliency objects and clear details of the source images, and the method we proposed has a better performance than other state-of-the-art (SOTA) methods.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/d2c679ee850c97c34439387492a42b3342c63628
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach (2024, methodology): “Multi-stage fusion methods [20][27][28][29] involve separate processing of the input images before combining them, whereas end-to-end fusion methods [30][24]combine the input images directly.”