Adaptive Subtraction for c-SIM Microscopy Image Processing
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Solution Overview
Problem
Current super-resolution microscopy techniques face challenges in producing consistent high-quality images due to arbitrary user settings, leading to potential image degradation and incomplete utilization of improved resolution, especially when dealing with objects of variable intensity.
Innovation Solution
The method involves using computer-implemented image processing to scale and subtract pixel intensities from non-toroidal and toroidal beam image components, eliminating the need for user-defined parameters by adapting the scaling based on peak intensities, thereby forming a super-resolution image without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If arbitrary user settings are used for image re-construction, then the user has flexibility in parameter selection, but image consistency and quality deteriorate
Solution Approach 1:
The system performs self-calibration by automatically determining the scaling factor through image analysis. The processor identifies peak intensities in both toroidal and non-toroidal beam images and computes the scaling factor as their ratio, eliminating the need for user-defined parameters while ensuring consistent and reliable image reconstruction across different experiments and instruments.
2Manufacturing precision
If user-defined parameters are required for image re-construction, then parameter optimization is possible, but operational complexity and skill requirements increase
Solution Approach 1:
The system automatically determines optimal re-construction parameters through self-calibration. The processor calculates the scaling factor by comparing peak intensities from the toroidal and non-toroidal beam images, eliminating the need for user expertise in parameter optimization while maintaining high image re-construction quality.
Solution Approach 2:
The system dynamically adjusts the scaling factor parameter based on the actual image data. By computing the scaling factor as the ratio of peak intensities from the two beam types, the system adapts to different imaging conditions and sample characteristics without requiring manual parameter adjustment, thereby simplifying operation while preserving precision.
3Productivity
If fixed scaling factors are used for image subtraction, then processing speed is maintained, but image quality and resolution utilization deteriorate
Solution Approach 1:
The system performs preliminary image analysis to determine peak intensities and calculate the scaling factor before the actual image subtraction. This preliminary calibration step ensures that the scaling factor is optimized for the specific image data, maximizing resolution utilization and image quality while maintaining efficient processing through automated computation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the reliability and usability of super-resolution microscopy by accurately resolving objects with varying intensities, improving lateral resolution and reducing artifacts, and can be applied to various microscopes without requiring advanced technical skills or training.
Implementation Method 1
computer-implemented image processing methods use a computer processor and computer memory, and include accessing, from the computer memory, a non-toroidal beam image component comprising a set of pixel intensities across an imaging area and a toroidal beam image component comprising a set of pixel intensities across the imaging area
Implementation Method 2
scaling, with the computer processor, an image intensity of at least one pixel of one of the non-toroidal or toroidal beam image components by a ratio between a peak non-toroidal beam imaging pixel intensity across the imaging area and a peak toroidal beam imaging intensity across the imaging area to produce a scaled image intensity
Implementation Method 3
determining a difference between the scaled image intensity of the at least one pixel of the non-toroidal or toroidal image components and an image intensity of at least one pixel of the other of the non-toroidal or toroidal image components to form at least a portion of an image
Implementation Method 4
acquiring the non-toroidal beam image component and the toroidal beam image component by directing a non-toroidal beam to a sample area and detecting non-toroidal beam induced response light from the sample area
Implementation Method 5
the formed image is a super-resolution image. In some examples, the super-resolution image is a Raman scattering image
Implementation Method 6
directing a toroidal beam to the sample area comprises directing a source beam through a vortex phase plate to produce the toroidal beam
Implementation Method 7
the peak non-toroidal imaging pixel intensity comprises a highest intensity below a saturating level of a detector used to obtain the non-toroidal image component
Data Source
AI summary
Computer-implemented image processing methods with a computer processor and computer memory, comprise accessing, from the computer memory, a non-toroidal beam image component comprising a set of pixel intensities across an imaging area and a toroidal beam image component comprising a set of pixel intensities across the imaging area, scaling, with the computer processor, an image intensity of at least one pixel of one of the non-toroidal or toroidal beam image components by a ratio between a peak non-toroidal beam imaging pixel intensity across the imaging area and a peak toroidal beam imaging intensity across the imaging area to produce a scaled image intensity, and determining a difference between the scaled image intensity of the at least one pixel of the non-toroidal or toroidal image components and an image intensity of at least one pixel of the other of the non-toroidal or toroidal image components to form at least a portion of an image.