TY - GEN
T1 - A Hybrid Supervised Fusion Deep Learning Framework for Microscope Multi-Focus Images
AU - Yang, Qiuhui
AU - Chen, Hao
AU - Jiang, Mingfeng
AU - Wang, Mingwei
AU - Zhang, Jiong
AU - Sun, Yue
AU - Tan, Tao
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - The quality of multi-focus microscopic image fusion hinges upon the precision of the image registration technology. However, algorithms for registration tailored specifically for multifocal microscopic images are lacking. Due to the presence of fuzzy regions and weak textures of multi-focus microscope images, the registration of patches is suboptimal. For these problems, this paper formulates a hybrid supervised deep learning model. It can improve the accuracy of registration and fusion. The generalization ability of the model to the actual deformation field enhance by the artificial deformation field. A step of patch movement simulation is employed to blur the multi-focus microscopic images and make synthetic flow, thus emulating distinct fuzzy regions in the two images to be registered, consequently enhancing the model's generalization ability. The experiments demonstrate that our proposed approach is superior to the existing registration algorithms and improves the accuracy of image fusion.
AB - The quality of multi-focus microscopic image fusion hinges upon the precision of the image registration technology. However, algorithms for registration tailored specifically for multifocal microscopic images are lacking. Due to the presence of fuzzy regions and weak textures of multi-focus microscope images, the registration of patches is suboptimal. For these problems, this paper formulates a hybrid supervised deep learning model. It can improve the accuracy of registration and fusion. The generalization ability of the model to the actual deformation field enhance by the artificial deformation field. A step of patch movement simulation is employed to blur the multi-focus microscopic images and make synthetic flow, thus emulating distinct fuzzy regions in the two images to be registered, consequently enhancing the model's generalization ability. The experiments demonstrate that our proposed approach is superior to the existing registration algorithms and improves the accuracy of image fusion.
KW - Fusion
KW - Multi-focus microscope images
KW - Supervised registration
UR - http://www.scopus.com/inward/record.url?scp=85180797565&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-50078-7_17
DO - 10.1007/978-3-031-50078-7_17
M3 - Conference contribution
AN - SCOPUS:85180797565
SN - 9783031500770
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 210
EP - 221
BT - Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
A2 - Sheng, Bin
A2 - Bi, Lei
A2 - Kim, Jinman
A2 - Magnenat-Thalmann, Nadia
A2 - Thalmann, Daniel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 40th Computer Graphics International Conference, CGI 2023
Y2 - 28 August 2023 through 1 September 2023
ER -