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ORIGINAL RESEARCH article
Front. Phys.
Sec. Radiation Detectors and Imaging
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1575606
This article is part of the Research Topic Multi-Sensor Imaging and Fusion: Methods, Evaluations, and Applications, Volume III View all 6 articles
Multi-focus image fusion based on pulse coupled neural network and WSEML in DTCWT domain
Provisionally accepted- Xinjiang University, Urumqi, China
The goal of multi-focus image fusion is to merge near-focus and far-focus images of the same scene to obtain an all-focus image that accurately and comprehensively represents the focus information of the entire scene. The current multi-focus fusion algorithms lead to issues such as the loss of details and edges, as well as local blurring in the resulting images. To solve these problems, a novel multifocus image fusion method based on pulse coupled neural network (PCNN) and weighted sum of eight-neighborhood-based modified Laplacian (WSEML) in dual-tree complex wavelet transform (DTCWT) domain is proposed in this paper. The source images are decomposed by DTCWT into low-and high-frequency components, respectively; then the average gradient (AG) motivate PCNNbased fusion rule is used to process the low-frequency components, and the WSEML-based fusion rule is used to process the high-frequency components; we conducted simulation experiments on the public Lytro dataset, demonstrating the superiority of the algorithm we proposed.
Keywords: Multi-focus image, image fusion, DTCWT, PCNN, WSEML
Received: 12 Feb 2025; Accepted: 05 Mar 2025.
Copyright: © 2025 Jia and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Tiande Ma, Xinjiang University, Urumqi, China
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