@inproceedings{2c5940a1960a495fa86553546537241a,
title = "COMPLEMENTARY FUSION NETWORK BASED ON FREQUENCY HYBRID ATTENTION FOR PANSHARPENING",
abstract = "Pansharpening is a feasible way to obtain the high-resolution (HR) multispectral (MS) images by using panchromatic (PAN) images to sharpen low-resolution MS images. Despite its great advances, most existing pansharpening methods neglect the importance of integrating local and non-local characteristics of images, resulting in the imbalance of spatial and spectral distribution. In this paper, we propose a complementary fusion network (CFNet) based on frequency hybrid attention mechanism for pansharpening. By introducing the frequency transformation and the deformable cross-attention, our model takes image-wide receptive field into consideration to explore global feature learning. Combined with the convolutional layers with local receptive field, CFNet can well capture local and non-local features. Experimental results demonstrate that the proposed method outperforms the comparison methods in terms of visual and quantitative qualities.",
keywords = "cross-attention, deep learning, frequency domain, image fusion, pansharpening",
author = "Yinghui Xing and Litao Qu and Kai Zhang and Yan Zhang and Xiuwei Zhang and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSP48485.2024.10446416",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2650--2654",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
}