Multiview Processing - Zeiss LSM 880 Operating Manual

ZEISS
Fig. 518 Processing – Lightsheet Processing.

Multiview Processing,

Deconvolution
Two tabs are available for detailed configuring (Deconvolutions tab and PSF settings tab):
On the Deconvolution tab, select the desired algorithm and define the precise settings for it.
Use the drop-down list next to Algorithm to choose from the available deconvolution algorithms:
Nearest Neighbour: The Nearest neighbor method uses the simplest and fastest algorithm (Castleman,
K.R., Digital image Processing, Prentice-Hall, 1079). Its function is based on subtraction of the out-focus
information in each plane of a stack, taking the neighboring sections above and below the corrected Z-
plane into account. This method is applied sequentially to each plane of the entire Z-Stack. It allows you
to enhance contrast quickly, even if image stacks have not been put together optimally.
Regularized Inverse: The inverse filter is a genuine 3D method and generally achieves better results
than the Nearest neighbor algorithm. It essentially involves dividing the Fourier transformation ("FT") of
the volume by the FT or the PSF, which can be performed very quickly. In the real space, this corresponds
to a deconvolution. In addition, a statistical method ("General Cross Validation – GCV") is applied which
determines the noise component of the image and automatically sets the restoration strength to the
optimum level in line with this. This process is also known as regularization. Z-Stacks must have been
acquired at the correct (Nyquist or smaller) distance. The additional acquisition of z-planes above and
below the structure of interest is recommended.
364
CHAPTER 1 - SYSTEM OPERATION
Left Tool Area and Hardware Control Tools
000000-2071-464
5.4.19.2 Multiview Processing
5.4.19.2.1 Deconvolution
For the multiview datasets acquired with the
Lightsheet Z.1, Deconvolution is an available
option (Fig. 518).
The following datasets are available for this
processing option:
a) Multiview experiments before registration
and fusion (single or dual side illumination)
In this case, deconvolution is performed for each
frame from all Z-Stacks and all Views. The process
uses a point spread function (PSF) valid for single
view images.
b) Multiview experiments after registration
and fusion.
The deconvolution is performed on the fused
dataset, hence only one Z-Stack needs to be
processed. It uses a different point spread function
(PSF) than what is used for the deconvolution
before registration and fusion of a multiview
experiment dataset.
The software automatically recognizes if the image
has been registered and fused or not and adjusts
the interfaces accordingly.
LSM 880
10/2014 V_01