Résumé: 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.

Langue: Anglais