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Complex wavelets for extended depth‐of‐field: A new method for the fusion of multichannel microscopy images
Author(s) -
Forster Brigitte,
Van De Ville Dimitri,
Berent Jesse,
Sage Daniel,
Unser Michael
Publication year - 2004
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.20092
Subject(s) - wavelet , image fusion , artificial intelligence , wavelet transform , depth of field , computer vision , focus (optics) , fusion , microscopy , computer science , optics , field (mathematics) , image (mathematics) , pattern recognition (psychology) , mathematics , physics , linguistics , philosophy , pure mathematics
Microscopy imaging often suffers from limited depth‐of‐field. However, the specimen can be “optically sectioned” by moving the object along the optical axis. Then different areas appear in focus in different images. Extended depth‐of‐field is a fusion algorithm that combines those images into one single sharp composite. One promising method is based on the wavelet transform. Here, we show how the wavelet‐based image fusion technique can be improved and easily extended to multichannel data. First, we propose the use of complex‐valued wavelet bases, which seem to outperform traditional real‐valued wavelet transforms. Second, we introduce a way to apply this technique for multichannel images that suppresses artifacts and does not introduce false colors, an important requirement for multichannel optical microscopy imaging. We evaluate our method on simulated image stacks and give results relevant to biological imaging. Microsc. Res. Tech. 65:33–42, 2004. © 2004 Wiley‐Liss, Inc.