High-Resolution Image Creation via Low-Resolution Superposition
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Solution Overview
Problem
Scanning imaging methods in endoscopy and endomicroscopy face challenges in achieving high frame rates while maintaining high resolution, as the long scanning process required for high resolution results in low frame rates, and reducing resolution to increase frame rate compromises image quality.
Innovation Solution
A method that increases the pixel resolution of low-resolution images by registering and superimposing them to create a high-resolution image, utilizing the movement of the object or grid displacement to fill in missing information between grid points, allowing for higher resolution than when using all grid points, and enabling the creation of high-resolution images quickly and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all lines are used for scanning to achieve high resolution, then the resolution of the image is improved, but the scanning time increases and frame rate decreases
Solution Approach 1:
The patent performs preliminary registration and superimposition of multiple low-resolution images to create a high-resolution image. By preparing multiple low-resolution frames in advance and processing them through image fusion algorithms, the system achieves high resolution without requiring a complete high-resolution scan for each frame, thus improving frame rate while maintaining resolution quality.
Solution Approach 2:
The patent creates a high-resolution image by copying and combining information from multiple low-resolution images. The image fusion process copies relevant features and details from different low-resolution frames and synthesizes them into a single high-resolution output, effectively generating high-quality images without performing exhaustive high-resolution scanning.
2Productivity
If the scanning process is reduced to increase frame rate, then the frame rate is improved, but the resolution of the image deteriorates
Solution Approach 1:
The patent merges multiple low-resolution images through a superimposition process to create a single high-resolution image. By combining the information from multiple frames that were captured with reduced scanning, the system reconstructs fine details and achieves high resolution output, thereby maintaining image quality while enabling faster frame rates.
Solution Approach 2:
The patent transitions from the temporal dimension (frame rate) to the spatial dimension (image fusion). Instead of increasing the scanning speed in time, the system uses multiple low-resolution frames captured over time and processes them spatially through registration and superimposition algorithms, effectively trading temporal resolution for spatial resolution enhancement.
3Productivity
If the image section is reduced to achieve fluid video stream, then the frame rate is improved, but the observable area of the object is reduced
Solution Approach 1:
The system performs preliminary registration of multiple low-resolution images to align them accurately across the entire observable area. This preliminary alignment enables subsequent superimposition to reconstruct high-resolution images covering the full field of view without requiring reduced scanning, thus maintaining both frame rate and observable area.
Data Source
AI summary
A method for creating a high-resolution image of an object from low-resolution images of the object is provided. Both the low-resolution images and the high-resolution image are composed of a pixel grid. An image recording device successively records low-resolution images, in which pitches of the grid points of the pixel grid are increased in one image dimension in comparison with the pitches of the grid points of the pixel grid in the high-resolution image to be created. A data processing system registers the low-resolution images with respect to one another to obtain registered images which are superimposed to obtain the high-resolution image. The grid points of the low-resolution images and the grid points of the high-resolution image have same dimensions and the data processing system uses image information obtained from different positions of the object relative to the grid points in the individual low-resolution images to create the high-resolution images.