Window-Based Parallel Processing for Super-Resolution Microscopy
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Solution Overview
Problem
Fluctuation-based super-resolution imaging techniques face significant computational burdens due to the need for extensive statistical analysis, limiting their application in producing high-resolution images efficiently.
Innovation Solution
A method and system that decompose image data into windows, process these windows in parallel using multiple processors to calculate eigenimages and indicator values, allowing for efficient production of super-resolution images by leveraging temporal intensity patterns and noise suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluctuation-based super-resolution imaging techniques are used to achieve high-resolution images, then image resolution is improved, but computational burden increases
Solution Approach 1:
The patent divides the image data into multiple windows, where each window contains a subset of spatially-coincident sections from different time points. This segmentation allows the computational workload to be distributed across multiple processors, reducing the computational burden on any single system while maintaining the ability to achieve super-resolution through statistical analysis of temporal fluctuations.
2Measurement precision
If statistical analysis of temporal fluctuations is performed to produce super-resolution images, then image resolution is improved, but processing time increases
Solution Approach 1:
By segmenting the image data into windows that can be processed independently, the patent enables parallel computation across multiple processors. This reduces the total processing time while maintaining the statistical analysis necessary for super-resolution.
Solution Approach 2:
The patent processes only the necessary portions of the image data (spatially-coincident sections) rather than the entire image stack sequentially. This partial action approach reduces processing time while still capturing the essential temporal fluctuations needed for super-resolution reconstruction.
3Device complexity
If the entire image stack is processed sequentially, then computational resources are simplified, but productivity decreases
Solution Approach 1:
The patent segments the image stack into multiple windows that can be processed in parallel by multiple processors. This increases productivity by utilizing multiple computational resources simultaneously, while the segmented structure keeps each individual processing task manageable and the overall system architecture relatively simple.
Solution Approach 2:
The patent introduces a temporal dimension to the processing by organizing data into windows that capture spatially-coincident sections across different time points. This dimensional organization enables parallel processing while maintaining the relationships needed for super-resolution reconstruction.
Data Source
AI summary
A computer-implemented method of processing image data to produce an output image is provided, where the method comprises receiving image data comprising a stack of images, captured at different times, of part or all of a sample region containing a sample; selecting from the stack of images a plurality of windows, each window comprising a respective stack of spatially-coincident sections of the images, where each window is processed to determine indicator values for test points representative of a likelihood of a part of said sample being present at a location of the sample region corresponding to the test point, where the indicator values are combined to produce an output image of the part or all of the sample region.