@inproceedings{7f03a75295314d3e80c8cb5d16f726b2,
title = "FEATURE DECOMPOSITION TRANSFORMERS FOR INFRARED AND VISIBLE IMAGE FUSION",
abstract = "We propose an infrared and visible image fusion algorithm using modality-shared and modality-specific feature decomposition transformers. First, the proposed algorithm extracts multiscale shallow features of infrared and visible images. Then, we develop modality-shared and modality-specific feature decomposition transformers that decompose the features into common and complementary components for each modality. For better decomposition, we develop a decomposition loss by constraining the common features to be correlated while the complementary features are uncorrelated. Finally, the reconstruction block generates the fused image by combining the common and complementary features. Experimental results show that the proposed algorithm significantly outperforms conventional algorithms on several datasets.",
keywords = "contrastive learning, feature decomposition, transformer, Visible and infrared image fusion",
author = "Gahyeon Kim and Vien, \{An Gia\} and Nguyen, \{Duong Hai\} and Chul Lee",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE; 31st IEEE International Conference on Image Processing, ICIP 2024 ; Conference date: 27-10-2024 Through 30-10-2024",
year = "2024",
doi = "10.1109/ICIP51287.2024.10647365",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2662--2668",
booktitle = "2024 IEEE International Conference on Image Processing, ICIP 2024 - Proceedings",
address = "United States",
}