MODULE
AutoQuant Deconvolution
Industry-leading Image Restoration
Clear images in an instant
The best of AutoQuant standalone software has been upgraded, lab tested, GPU-accelerated by default, simplified into a single intuitive dialog, and is ready to fit into your imaging workflow.
The AutoQuant Deconvolution Module delivers repeatable, quality results in only a fraction of the time. Restore image fidelity, enhance quality, and reverse distortions introduced during the image acquisition process.
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Why Deconvolve Fluorescent Images?
Convolution + Noise = a ‘Blurry’ image
AutoQuant Deconvolution greatly improves both the image resolution and contrast, leading to enhanced visualization, better measurements, and more meaningful analysis. To do this, both the convolution and noise are handled to take your images from blurry to beautiful.
Convolution
When light waves encounter an object – such as the sample of interest, a lens, or even air molecules – they scatter & bend. Within an optical system, such as a microscope, this effect on a light wave is called the point-spread function, commonly abbreviated to PSF.
The PSF applies to every point of light passing through the optical system, in a process called convolution.
Noise
Digital images invariably suffer from the addition of some amount of noise that is introduced through a combination of ambient factors and the materials and electronics present in the digital camera hardware.
Signal-dependent noise can be characterized by a Poisson distribution, while noise arising from the imaging system often follows a Gaussian distribution.
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Intuitive Guided Process
AutoQuant is still the most intuitive deconvolution software in the industry, using a simple yet elegant workflow to direct any user through the necessary steps to achieve repeatable image restoration.
01
Confirm Image Metadata
02
Confirm Channel Metadata
03
Choose Starting PSF
04
Choose Deconvolution Settings
Deconvolve!
Straightforward Point-Spread Function (PSF) Models for Common Microscopy Modalities
The Deconvolution Cycle
Step #1
Start with a PSF estimate, either calculated from optics or input as a bead image.
Step #2
Create an initial image estimate using the original image, which may be inverse filtered. Convolve the image estimate with the PSF estimate to create a re-blurred estimate.
Step #3
Compare the re-blurred estimate against the original image to determine the adjustment to be made to the image estimate.
Step #4
Update the image estimate and amplify it until it no longer improves. After several iterations, the difference between the original image and the re-blurred estimate becomes minimal and the original image is deconvolved.
Time tested and still trustworthy
While integrating the time-tested deconvolution algorithm in the new Image-Pro module, we conducted a series of laboratory tests to ensure the new integration is still trustworthy and that it still delivers reliable, repeatable results, even within a new platform. The whitepaper below details the results and conclusions of those tests.
In short, all of our new tests yielded reassuring outcomes. The AutoQuant Fixed PSF and Adaptive PSF algorithms continue to deliver consistent improvement and reliable data in all cases, especially for 3D data where we observed nearly identical improvement. Indeed, none of our tests showed a significant difference in results. Therefore, the new module has been streamlined to focus on the Fixed PSF algorithm alone for 3D images, making setup faster and generating the same quality data as previous AutoQuant versions.
Deconvolve Images Faster with
Built-in GPU Acceleration
For computationally-intensive applications like deconvolution, being able to process thousands of mathematical calculations at once is preferable to processing a few at a time. In short, you have more bandwidth available when you utilize the GPU rather than the CPU allowing for faster processing of the image.
Image Size
Deconvolution Time
- GPU Accelerated
- CPU Only
The CPU-GPU Collaboration
Certain portions of the deconvolution process still occur on the CPU, notably the initial setup. Once this is complete, AutoQuant accesses the many GPU processor cores that carry out the actual iterative deconvolution. The final results are then transferred back to the CPU and saved.
Batch Processing
Use Image-Pro’s native Batch Processing to select a folder of similar images to be deconvolved.
Save valuable time. Set up the batch and walk away.
Deconvolve groups of images from the same experiment without the need to write custom macros.
Choose either a fixed or adaptive PSF and apply it to the whole batch.
Automatically save deconvolved datasets for review and analysis.
AutoQuant
Image-Pro
Use it alone or combine it with Image-Pro modules for an even more powerful solution.
How does the new Module stack up against the older Standalone software?
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Macro language |
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OME file and metadata support |
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Theoretical PSF for STED Modality |
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3D Movie Maker |
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3D Viewer |
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Image Adjustments and Correction |
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Image Import and Set Builder |
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Spherical Aberration Correction |
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Batch Processing |
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GPU Acceleration |
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Inverse Filter |
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No/Nearest Neighbors Algorithm |
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DIC Restoration |
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* = Optional
Like What You See?
Get Started with Image-Pro for AutoQuant Deconvolution.