DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion

• URL: http://arxiv.org/abs/2409.10080v1
• Date: Mon, 16 Sep 2024 08:37:09 GMT
• Title: DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion
• Authors: Yuchen Guo, Ruoxiang Xu, Rongcheng Li, Zhenghao Wu, Weifeng Su,
• Abstract summary: Two-phase discriminative autoencoder framework, DAE-Fuse, generates sharp and natural fused images. Experiments on public infrared-visible, medical image fusion, and downstream object detection datasets demonstrate our method's superiority and generalizability.
• Score: 10.713089596405053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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