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:
Recovered Beads
Widefield image of subresolution beads before (left) and after (right) deconvolution. Data courtesy of Say-Tar Goh, CalTech, USA.

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.

Raw Widefield
Huygens Deconvolved
Maximum Intensity Projection of a deconvolved vs. raw widefield image. Data courtesy of Dr. Alexia Ferrand, Imaging Core Facility, Biozentrum, University of Basel.

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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HeLa deconvolution

Deconvolution questions

Raw data gives a distorted view of your object due to destructive convolution. Deconvolution is therefore fundamental to reliable image analysis. Huygens True Deconvolution ensures that the image intensity is conserved over its entire range so that bright objects are not falsely amplified or dim objects neglected. In addition, the quality is constant over the entire image, even when the image is divided and deconvolved in smaller bricks. More information on Huygens True Deconvolution can be found in the whitepaper on quantitative analysis of deconvolution results and on the webpage Huygens Deconvolution.

The attainable resolution improvement after deconvolution is strongly dependent on the microscope type, the signal-to-noise ratio and the recording conditions (most importantly: the sampling rate). It will therefore differ per image: for high-signal images, the resolution typically increases by a factor 2 in the axial direction (z) and slightly under 2 in the lateral directions (x and y) when using a theoretical PSF; for noisy images, the increase is more modest, but these images benefit greatly from the noise correction and signal increase resulting from Huygens Deconvolution. In any case, the largest resolution increase is achieved by using a measured PSF. Published work* reveals that resolution increases by nearly a factor 4 axially and over 2 laterally can then be achieved.

*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")838383
Bead image270265790
Restored bead image11693221
Resolution increasex 2.3x 2.8x 3.6
Data and table description taken from paper with permission.
Huygens Deconvolution is optimized for fast performance without compromising quality. The exact computation time depends on several factors, including:
  • 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.
Huygens Deconvolution works well on high to very low signal data as the MLE algorithms effectively remove noise while improving contrast. This means that the acquisition time can be descreased without loss of information in the final image. A big benefit is then that the sampling rate can be increased without time loss or risk of photobleaching due to prolonged light exposure. Experimental evidence can be found in the whitepaper on quantitative analysis of deconvolution results.

Yes. Huygens treats the image as the only known plane of a 3D stack and proceeds as usual. Before deconvolving, go to the Microscopic Parameters and set the z-sampling distance to the Nyquist rate as explained in 'sampling densities' (Huygens User Guide). For optimal results we do advise to image in 3D when possible, especially when performing quantitative analysis after deconvolution.