Microscopic Image Acquisition With Fewer Wavelength Samples
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Solution Overview
Problem
Computational imaging methods face significant overhead in acquisition and reconstruction times due to the need for multiple low-resolution images and wavelength-dependent processing, particularly in color imaging, which can degrade the quality and utility of high-resolution images.
Innovation Solution
Acquire a first image dataset using a first set of illumination conditions for a first wavelength with a larger number of conditions, and a second image dataset using a second set of illumination conditions with fewer conditions, combining these datasets to generate a computationally reconstructed image, thereby reducing acquisition and reconstruction times without significantly degrading image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple low-resolution images are acquired with varying illuminations to produce high-resolution computationally reconstructed images, then image resolution is improved, but acquisition time and computational overhead increase significantly
Solution Approach 1:
The patent applies partial action by acquiring a limited subset of illumination conditions rather than exhaustive sampling. Specifically, it uses a first set of illumination conditions for a first wavelength and a second set for a second wavelength, where the second set has fewer conditions than the first. This selective acquisition reduces overhead while maintaining sufficient image quality through computational reconstruction.
Solution Approach 2:
The patent changes parameters by varying illumination conditions (wavelength, illumination angle, polarization) across different image acquisitions. By systematically varying these parameters and using computational algorithms, the system reconstructs high-resolution images from multiple low-resolution inputs with different illumination states, thereby achieving high resolution without requiring exhaustive sampling of all possible illumination conditions.
2Measurement precision
If multiple low-resolution images are acquired with varying illuminations to produce high-resolution computationally reconstructed images, then image resolution is improved, but computational reconstruction time increases
Solution Approach 1:
The patent reduces computational reconstruction time by performing partial action - acquiring images under a limited number of illumination conditions rather than exhaustive sampling. The first wavelength uses a first set of illumination conditions and the second wavelength uses a second set with fewer conditions, reducing the total computational burden while maintaining reconstruction quality through selective parameter variation.
Solution Approach 2:
The patent segments the computational reconstruction process by handling different wavelengths separately with different numbers of illumination conditions. The first image dataset from the first wavelength is processed with a first set of illumination conditions, and the second image dataset from the second wavelength is processed with a second set of illumination conditions. This segmentation allows optimized processing for each wavelength channel, reducing overall computational time.
3Measurement precision
If color imaging processes operate with three color channels (RGB) to capture full color information, then image quality is improved, but acquisition time and computational reconstruction time are multiplied by a factor of three
Solution Approach 1:
The patent applies partial action by not requiring full exhaustive sampling for all color channels. Instead of acquiring the same number of illumination conditions for all three RGB channels, it uses a first set of illumination conditions for the first wavelength (e.g., green channel which contains more information) and a reduced second set for the second wavelength (e.g., red or blue channel). This selective approach reduces acquisition time while maintaining sufficient color image quality.
Solution Approach 2:
The patent changes parameters by varying the number of illumination conditions based on wavelength priority. The first wavelength (typically green in visible light) uses more illumination conditions as it contains more information, while the second wavelength uses fewer conditions. This parameter variation optimizes the trade-off between color image quality and acquisition efficiency across different color channels.
Data Source
AI summary
A microscope for computational imaging may include an illumination source configured to illuminate a sample with a plurality of wavelengths, an image sensor, an objective lens to image the sample onto the image sensor, and a processor operatively coupled to the illumination assembly and the image sensor. The processor may be configured to acquire a first image dataset from the sample illuminated using a first set of illumination conditions at a first wavelength. The processor may also be configured to acquire a second image dataset from the sample illuminated using a second set of illumination conditions having a second number of illumination conditions at a second wavelength. The second set of illumination conditions comprises fewer illumination conditions than the first set in order to decrease acquisition time. The processor may be configured to combine the first and second image datasets into a computationally reconstructed image of the sample.