Additional Modules

Expand the capabilities of AutoQuant X3

Easy Implementation

Easily Export to and from Image-Pro Premier

Faster Response, Same Easy to Use Interface

AutoQuant X3 GPU Module delivers even faster response times than AutoQuant X3 without sacrificing the ease of use that is inherent in the AutoQuant Platform. This removes the need for extensive additional training of users and removes the downtime to obtain immediate results.

With a single click, switch between CPU based to GPU based deconvolution, increase your response time, decrease your training time, and get the full benefit of AutoQuant X3 GPU module.

Supported Modalities

ModalityWidefieldConfocalConfocal and Widefield
Widefield Fluorescence  
Structured Illumination  
Light Sheet (SPIM)  
Confocal Fluorescence  
Spinning Disk  

= Supported in Module

= Measured PSF

GPU Deconvolution

Understanding How it all Works

Although many in the industry refer to this as GPU based deconvolution out of necessity, 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 CPU and saved.

Autoquant GPU

Graphics Processing Units (GPUs) have evolved from simple display adapters into computational powerhouses for applications like image processing. As rendering demands have increased, specialized processors on the GPU became more generalized, faster, and more numerous. These changes culminated in the rise of general-purpose computation being performed on devices that had nominally been designed to make special effects in games and movies look as realistic as possible.

The individual processor cores on a GPU are considerably slower than most CPU processor cores, but what they lack in power, they more than make up in numbers. The core count on a high-end workstation’s CPU(s) will be typically be around 32. In contrast, the core counts reported on even consumer-grade GPUs number into the thousands.



For computationally-intensive applications like deconvolution, in which a series of mathematical calculations must be performed on each one of millions of individual voxels, being able to process thousands at once is preferable to processing a few at a time. In short, you have more bandwidth available when you utilize the GPU rather the CPU allowing for faster processing of the image.