Multi-Exposure Image Fusion via Deformable Self-Attention
Most multi-exposure image fusion (MEF) methods perform unidirectional alignment within limited and local regions, which ignore the effects of augmented locations and preserve deficient global features. In this work, we propose a multi-scale bidirectional alignment network via deformable self-attenti...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 32(2023) vom: 04., Seite 1529-1540 |
|---|---|
| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2023
|
| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Résumé: | Most multi-exposure image fusion (MEF) methods perform unidirectional alignment within limited and local regions, which ignore the effects of augmented locations and preserve deficient global features. In this work, we propose a multi-scale bidirectional alignment network via deformable self-attention to perform adaptive image fusion. The proposed network exploits differently exposed images and aligns them to the normal exposure in varying degrees. Specifically, we design a novel deformable self-attention module that considers variant long-distance attention and interaction and implements the bidirectional alignment for image fusion. To realize adaptive feature alignment, we employ a learnable weighted summation of different inputs and predict the offsets in the deformable self-attention module, which facilitates that the model generalizes well in various scenes. In addition, the multi-scale feature extraction strategy makes the features across different scales complementary and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art MEF methods |
|---|---|
| Description: | Date Revised 04.04.2025 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2023.3242824 |