Affordable, Clear Images In A Fraction Of The Time
As the preferred deconvolution standard, AutoQuant X3, is the most complete package of 2D and 3D restoration algorithms available.
AutoQuant X3 makes it simple to deconvolve image sets and visualize them in time, Z, and channel, and analyze all parameters within the same, easy to use application.
Now, the easiest to use, most reliable deconvolution package on the market just got better. Introducing Graphics Processing Unit driven Deconvolution for AutoQuant X3. Adding the module to the current AutoQuant X3 platform maintains the current ease of use, while adding the speed of GPU processing to your current platform.
Click below to learn more about the AutoQuant X3 GPU Module
Why do you need Deconvolution?
The Deconvolution process restores the fidelity and enhances the quality of images which have undergone inherent, and often inevitable, distortions during the image acquisition process. The inherent optical limitations of microscopes, combined with sample characteristics and imaging techniques, often introduce blurring and other types of noise, which compromise image quality.
AutoQuant’s deconvolution tools greatly improve both the image resolution and its contrast, leading to enhanced visualization, better measurements, and more meaningful analysis.
Try AutoQuant X3 out FREE for 14 days.
Powerful image analysis software is the secret to great discoveries in any field. We have been customizing AutoQuant X for over 20 years! Let us prove it to you.
We will not only give you the software to use, but we’ll give you the grand tour FREE.
Deconvolve images with the most powerful Maximum Likelihood Estimation (MLE) available.
Easily add a GPU module to your AutoQuant X3.1 platform to decrease processing time and increase your throughput.
Quickly load multiple image sets to be automatically aligned and deconvolved sequentially.
Faster Deconvolution Times
Objective, Camera, and Dye Lists
Save your individual settings
Simplify your choices
Optimize your time
GPU Based Deconvolution
UNDERSTANDING HOW IT ALL WORKS
A “first guess” at the unblurred object is made, typically either by processing the observed image through an inverse filter, or by simply using the Observed Image itself. This becomes the initial Object Estimate.