Complex wavelets for extended depth‐of‐field: A new method for the fusion of multichannel microscopy images

By B. Forster, D. Van de Ville, Jesse Berent, Daniel Sage, Michael A. Unser. Microscopy research and technique (Print), 2004

398 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/f674fdef39d5bdfbe36bb8585db3b24bd3bbb601

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Citations in the corpus

Performance comparison of different multi-resolution transforms for image fusion (2011, neutral): “…including the pyramid decomposition [4] [5] [13], discrete wavelet (DWT) [6–8,14], stationary wavelet (SWT) [15] [16], dual-tree complex wavelet (DTCWT) [18] [19] [20] [21], curvelet (CVT) [22– 28], contourlet (CT) [29] [30] [31], and nonsubsampled contourlet transform (NSCT) [32] [33] [34].”
Multifocus Image Fusion and Restoration With Sparse Representation (2010, methodology): “The usually used multiscale transforms include various pyramids [14]–[17], discrete wavelet transform (DWT) [1], [5], [18], complex wavelet [19], [20], ridgelet [21], curvelet transform (CVT) [22], and contourlet [23].”
The ImageJ ecosystem: an open platform for biomedical image analysis (2015, methodology): “Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin”