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.
cite
@article{64d645a13fda6d7f0631eae6, author={Zeyu Wang and Xiongfei Li and Libo Zhao and Haoran Duan and Shidong Wang and Hao Liu and Xiaoli Zhang}, pages={2529-2552}, title={When Multi-Focus Image Fusion Networks Meet Traditional Edge-Preservation Technology}, volume=131, year=2023,}