Microscopic Image Acquisition With Wavelength-Selective Compression
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Solution Overview
Problem
Computational imaging methods require significant acquisition and reconstruction times due to the need for multiple low-resolution images and wavelength-dependent processing, particularly in color imaging, which can be time-consuming and resource-intensive.
Innovation Solution
Acquire a first image dataset using a larger set of illumination conditions for a first wavelength and a second, smaller set of conditions for a second wavelength, combining these datasets to generate a computationally reconstructed image, optimizing the acquisition and reconstruction process.
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
Solution Approach 1:
The patent segments the image acquisition process by wavelength channels. Instead of acquiring multiple illumination images for each wavelength separately, the system acquires all illumination conditions for one wavelength first, then proceeds to the next wavelength. This segmentation reduces the total number of acquisitions needed while maintaining high-resolution reconstruction quality across multiple wavelengths.
Solution Approach 2:
The patent applies partial action by acquiring fewer illumination images for certain wavelength channels compared to others. Specifically, it uses a reduced set of illumination images for wavelengths where full illumination coverage is less critical, while maintaining comprehensive illumination sampling for wavelengths where it is most beneficial. This reduces overall acquisition time while preserving essential image quality.
2Measurement precision
If computational imaging algorithms process each wavelength separately with good physical system models, then reconstruction accuracy is improved, but reconstruction time and acquisition time are multiplied
Solution Approach 1:
The patent merges the processing of multiple wavelength channels by acquiring all illumination data for one wavelength before moving to the next. This combining approach allows the system to process wavelengths in batches rather than interleaving them, reducing the multiplicative effect on reconstruction time while maintaining the accuracy benefits of wavelength-specific processing.
3Loss of information
If a larger number of illumination conditions are used for one wavelength compared to another, then information content is improved for that wavelength, but acquisition complexity increases
Solution Approach 1:
The patent applies local quality by differentiating the number of illumination conditions applied to different wavelength channels. Instead of using the same number of illumination conditions for all wavelengths, the system tailors the illumination sampling density to the specific information content needs of each wavelength, using more illumination conditions for wavelengths that benefit most from them.
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.