DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion
<|reference_start|>DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion: Multi-modality image fusion aims to integrate complementary data information from different imaging modalities into a single image. Existing methods often generate either blurry fused images that lose fine-grained semantic information or unnatural fused images that appear perceptually cropped from the inputs. In this work, we propose a novel two-phase discriminative autoencoder framework, termed DAE-Fuse, that generates sharp and natural fused images. In the adversarial feature extraction phase, we introduce two discriminative blocks into the encoder-decoder architecture, providing an additional adversarial loss to better guide feature extraction by reconstructing the source images. While the two discriminative blocks are adapted in the attention-guided cross-modality fusion phase to distinguish the structural differences between the fused output and the source inputs, injecting more naturalness into the results. Extensive experiments on public infrared-visible, medical image fusion, and downstream object detection datasets demonstrate our method's superiority and generalizability in both quantitative and qualitative evaluations.<|reference_end|>
@article{guo2024dae-fuse:,
    title={DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality
  Image Fusion},
    author={Yuchen Guo, Ruoxiang Xu, Rongcheng Li, Zhenghao Wu and Weifeng Su},
    journal={arXiv preprint arXiv:2409.10080},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10080},
    primaryClass={cs.CV cs.AI}
}