Microscope Image Recovery via Pre-stored Transmission Matrix
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Solution Overview
Problem
Current imaging systems using turbid media face high computational complexity, memory usage, and time complexity due to the need for extensive data measurement and processing, which limits image quality and requires averaging numerous samples to mitigate noise and errors.
Innovation Solution
A microscope design incorporating turbid media at the light entrance portion with a lens system and an image acquisition device that utilizes a transmission matrix storage unit and image recovery through compressed sensing with sparse representation, allowing for improved image quality and reduced complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transmission matrices are measured using a large number of base input light waves to compensate for scattering, then image quality is improved, but computational complexity, memory use, and time complexity increase significantly
Solution Approach 1:
The patent pre-calculates and stores transmission matrices for various light components entering the light entrance portion in a transmission matrix storage unit before actual imaging. This preliminary computation allows the system to avoid real-time complex calculations, reducing computational complexity during image acquisition while maintaining high image quality through accurate scattering compensation.
Solution Approach 2:
The patent extracts only the essential transmission matrix data needed for image recovery and stores it in a dedicated storage unit. By separating the transmission matrix measurement and storage from the actual imaging process, the system reduces memory usage and computational complexity during operation while preserving the ability to recover high-quality images.
2Measurement precision
If transmission matrices are measured using a large number of base input light waves to compensate for scattering, then image quality is improved, but time complexity increases significantly
Solution Approach 1:
The transmission matrices are pre-calculated and stored before actual imaging operations. This allows the system to perform complex scattering compensation calculations in advance, reducing the time required during actual image acquisition and recovery processes while maintaining high image quality.
Solution Approach 2:
The patent uses compressed sensing with sparse representation to recover images from fewer measurements than traditional methods would require. By utilizing the sparsity of natural images in certain basis representations, the system achieves high-quality image recovery with reduced time complexity compared to measuring and processing a large number of base input light waves.
3Measurement precision
If incoherent samples are averaged to reduce speckles in reconstructed images, then image quality is improved, but the number of measurements and processing requirements increase
Solution Approach 1:
The patent employs compressed sensing techniques that enable high-quality image recovery from a reduced number of incoherent samples. By exploiting the sparsity property of images in transformed domains, the system avoids the need to average thousands of samples while still achieving speckle reduction and high image quality, significantly reducing the quantity of measurements required.
Solution Approach 2:
The patent replaces the traditional mechanical approach of averaging multiple samples with a computational approach based on compressed sensing and sparse representation. This substitution allows the system to recover high-quality images from fewer measurements by using mathematical optimization rather than simple averaging, reducing the number of measurements needed while maintaining 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
The solution reduces computational and memory complexity, enhances image quality, and enables faster image acquisition with fewer required samples, effectively overcoming the limitations of previous systems.
Implementation Method 1
a lens system receiving light emitted from a light source and containing image information of an observation object
Implementation Method 2
an output light wave passing through turbid media undergoes multiple scattering and is thus completely different in shape from an input light wave
Implementation Method 3
an image sensor acquiring information of light passing through the lens system
Data Source
AI summary
Disclosed herein is a microscope. The microscope includes: a lens system receiving light emitted from a light source and containing image information of an observation object; turbid media interposed between the observation object and the lens system; and an image acquisition device acquiring the image information, wherein the image acquisition device comprises: an image sensor acquiring information of light passing through the lens system; a transmission matrix storage unit previously storing a transmission matrix indicating a transmission state of various light components entering the light entrance portion; and an image recovery unit recovering the image information from the information of light acquired by the image sensor through compressed sensing using a sparse representation based on the transmission matrix. The microscope can provide improved image quality and can acquire an image through simple operation.