MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training
This addresses image fusion for applications like surveillance or medical imaging, but it is incremental as it builds on existing autoencoder and MAE techniques.
The paper tackles Infrared and Visible Image Fusion by proposing MaeFuse, a model that uses a pretrained Masked Autoencoder encoder and guided training to extract features without downstream tasks, achieving impressive performance on public datasets.
In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with downstream tasks to obtain highlevel visual information, which is effective in emphasizing target objects and delivering impressive results in visual quality and task-specific applications. Instead of being driven by downstream tasks, our model called MaeFuse utilizes a pretrained encoder from Masked Autoencoders (MAE), which facilities the omni features extraction for low-level reconstruction and high-level vision tasks, to obtain perception friendly features with a low cost. In order to eliminate the domain gap of different modal features and the block effect caused by the MAE encoder, we further develop a guided training strategy. This strategy is meticulously crafted to ensure that the fusion layer seamlessly adjusts to the feature space of the encoder, gradually enhancing the fusion performance. The proposed method can facilitate the comprehensive integration of feature vectors from both infrared and visible modalities, thus preserving the rich details inherent in each modal. MaeFuse not only introduces a novel perspective in the realm of fusion techniques but also stands out with impressive performance across various public datasets.