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When Multi-Focus Image Fusion Networks Meet Traditional Edge-Preservation Technology.
- Source :
-
International Journal of Computer Vision . Oct2023, Vol. 131 Issue 10, p2529-2552. 24p. - Publication Year :
- 2023
-
Abstract
- Generating the decision map with accurate boundaries is the key to fusing multi-focus images. In this paper, we introduce edge-preservation (EP) techniques into neural networks to improve the quality of decision maps, supported by an interesting phenomenon we found: the maps generated by traditional EP techniques are similar to the feature maps in the trained network with excellent performance. Based on the manifold theory in the field of edge-preservation, we propose a novel edge-aware layer derived from isometric domain transformation and a recursive filter, which effectively eliminates burrs and pseudo-edges in the decision map by highlighting the edge discrepancy between the focused and defocused regions. This edge-aware layer is incorporated to a Siamese-style encoder and a decoder to form a complete segmentation architecture, termed Y-Net, which can contrastively learn and capture the feature differences of the sourced images with a relatively small number of training data (i.e., 10,000 image pairs). In addition, a new strategy based on randomization is devised to generate masks and simulate multi-focus images with natural images, which alleviates the absence of ground-truth and the lack of training sets in multi-focus image fusion (MFIF) task. The experimental results on four publicly available datasets demonstrate that Y-Net with the edge-aware layers is superior to other state-of-the-art fusion networks in terms of qualitative and quantitative comparison. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE fusion
*FERTILITY preservation
*DECODING algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 09205691
- Volume :
- 131
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- International Journal of Computer Vision
- Publication Type :
- Academic Journal
- Accession number :
- 170028631
- Full Text :
- https://doi.org/10.1007/s11263-023-01806-w