Super-resolution Image Generation via S/N Ratio-Based Data Acquisition
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Solution Overview
Problem
Existing sample observation apparatuses struggle to generate good super-resolution images when the proportion of high-frequency components in image data is low, leading to insufficient Signal-to-Noise (S/N) ratio, which is essential for visualizing super-resolution components.
Innovation Solution
A sample observation apparatus that calculates and adjusts the number of image data sets to be added together based on the required S/N ratio and image acquisition conditions, using multiple sets of image data from the same region to generate a raw image with a high proportion of high-frequency components and reduced noise, thereby enabling the generation of good super-resolution images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sets of image data are acquired and added together to improve S/N ratio, then the quality of super-resolution image is improved, but the acquisition time and number of operations increase
Solution Approach 1:
The system pre-calculates the required number of image data sets based on the desired S/N ratio and image acquisition conditions before actually acquiring the images. This allows the operator to know in advance how many scans are needed, avoiding unnecessary repeated acquisitions and optimizing the balance between image quality and acquisition time.
Solution Approach 2:
The system provides feedback by calculating and displaying the required number of image data sets based on the target S/N ratio. This feedback mechanism guides the acquisition process, allowing the system to stop at the optimal point rather than requiring excessive acquisitions, thus reducing total acquisition time while maintaining required image quality.
2Measurement precision
If the number of image data sets is increased to ensure sufficient S/N ratio, then super-resolution components can be visualized, but the complexity of the observation process increases
Solution Approach 1:
The system performs self-service by automatically calculating the required number of image data sets based on pre-stored S/N ratio requirements and current acquisition conditions. This eliminates the need for operators to manually determine the optimal number of scans, simplifying the observation process while ensuring adequate S/N ratio for super-resolution visualization.
Solution Approach 2:
The system pre-stores the S/N ratio requirements needed for generating super-resolution images before the observation process begins. This preliminary preparation allows the system to automatically determine the appropriate number of acquisitions without requiring complex real-time calculations or operator intervention during the observation process.
3Ease of operation
If image data is acquired under fixed acquisition conditions, then the acquisition process is simple, but the S/N ratio may be insufficient for generating good super-resolution images
Solution Approach 1:
The system dynamically adjusts the number of image data sets to be acquired based on the relationship between the desired S/N ratio and the actual acquisition conditions. Rather than using fixed acquisition parameters, the system flexibly determines the appropriate number of scans by comparing required S/N ratio with conditions achieved during acquisition, ensuring adequate image quality while maintaining operational simplicity.
Solution Approach 2:
The system changes the parameter of the number of image data sets based on the target S/N ratio and acquisition conditions. By adjusting this parameter dynamically rather than fixing it in advance, the system ensures that the S/N ratio requirement is met while adapting to actual measurement conditions, thus resolving the contradiction between simplicity and image quality.
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 ensures a sufficient S/N ratio is achieved, allowing for the effective visualization of super-resolution components in the generated images, while eliminating the need for a troublesome task in generating raw images.
Implementation Method 1
an image-data acquisition section that acquires image data by detecting light from a sample
Data Source
AI summary
A sample observation apparatus includes a memory and a main controller. The main controller stores a first number of photoelectrons required in a raw image for generating a super-resolution image. The main controller calculates the number of image data sets to be added together for generating the raw image based on the first number of photoelectrons stored in the memory and a predetermined image acquisition condition, acquires multiple sets of image data of the same region of a sample by repeatedly detecting light from the same region based on the calculated number of image data sets, and generates the raw image by adding together the acquired multiple sets of image data of the same region.