Deconvolution
Restore the information lost in microscopy imaging
A microscopy image is not an exact copy of the object under the microscope: a so-called convolution of the object light leads to blurring in the resulting image. The inverse process, deconvolution, reverses this blurring and brings the image closer to the actual object.
Noise Correction - Deblurring - Better Resolution
The convolution is described by an initially unknown function that depends on the microscopy parameters. This function, the Point Spread Function (PSF), can be calculated using a theoretical optical computation or it can be based on prior knowledge, for example by recording beads. This PSF can then be used to reverse the convolution, i.e., perform a deconvolution:
Huygens offers both deconvolution with a theoretical PSF, accurately calculated from the image parameters, and with a measured PSF, distilled from bead images using the Huygens PSF Distiller. The PSF is fed into the most advanced algorithms that currently exist to restore the image.
For details on how the Huygens Software does deconvolution see Huygens Deconvolution.Testing Huygens Deconvolution Software
Interested in testing the latest version of the Huygens software? Do not hesitate to request a test license.
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*Kano, Hiroshi, Hans T.M. van der Voort, Martin Schrader, Geert M.P. van Kempen and Stefan W. Hell. (1996) Avalanche photodiode detection with object scanning and image restoration provided 2-4 fold resolution increase in two-photon fluorescence microscopy. BioImaging 4 '96 p187-197.
"Table description: The full-width-at-half-maxima (FWHM) in X, Y and Z direction for the bead images, the restored image, and a solid uniformly stained bead of 110 nm diameter. The restoration is performed by a PSF extracted from two bead images using the maximum-likelihood estimation algorithm."
X (nm) | Y (nm) | Z (nm) | |
Bead object ("true bead") | 83 | 83 | 83 |
Bead image | 270 | 265 | 790 |
Restored bead image | 116 | 93 | 221 |
Resolution increase | x 2.3 | x 2.8 | x 3.6 |
- Microscope type: images from widefield microscopes tend to require more iterations than those from confocal or 2-photon microscopes.
- Object type: sparse objects can be restored more effectively than dense objects. The more resolution gain is possible, the more iterations are needed, even if the iterations themselves also become more effective ('bigger steps').
- Noise: low noise makes a large resolution gain possible so then more iterations are needed.
- Algorithm: our Good's Rougnness MLE (Maximum Likelihood Estimation) needs less time than our Classic MLE; see Deconvolution Algorithms.
- Hardware: the number and type of CPU processors and GPU cards influence performance. Additionally, if the memory is insufficient, the processing speed depends on the type and specifications of virtual memory, as well as I/O performance.
For more information on this, visit the GPU Benchmarks page.