Fluorescence Image Unmixing With Poisson-Preserving Noise Reduction
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Solution Overview
Problem
Existing fluorescence imaging methods struggle with spectral overlap between dyes, leading to increased noise levels and loss of the Poisson noise law, which complicates image clarity and affects subsequent processing steps like denoising and deconvolution.
Innovation Solution
A device and method for unmixing images that involve determining a noise-map image, a signal-to-noise image, and a denoised signal-to-noise image to reduce noise, preserving Poisson characteristics, and using parallel processing steps with weighted inverses and wavelet filters to enhance image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear unmixing is applied to separate overlapping fluorescent dye signals, then image clarity and signal-to-noise ratio are improved, but noise level increases in pixels with co-localization of dyes
Solution Approach 1:
The patent applies denoising processing to the mixed fluorescence image before performing linear unmixing. This preliminary denoising step removes background noise and artifacts from the raw image, so that when unmixing is subsequently applied, the noise amplification problem in co-localized regions is significantly reduced while still maintaining the ability to separate overlapping dye signals
Solution Approach 2:
The patent introduces an intermediate processing step (denoising) between image acquisition and unmixing. This intermediate step acts as a mediator that prepares the image data by reducing noise before the unmixing operation, thereby preventing the noise amplification that would otherwise occur during the separation of fluorescent dye signals
2Object-affected harmful factors
If denoising is performed prior to unmixing, then noise is reduced effectively, but small deviations of filter behavior in different channels are amplified by linear unmixing leading to visible artifacts
Solution Approach 1:
The patent carefully selects and optimizes the denoising filter parameters to minimize channel-specific deviations. By adjusting parameters such as filter kernel size, sigma values, and threshold settings, the method reduces noise while maintaining consistent filter behavior across all fluorescence channels, thereby preventing artifact generation during subsequent unmixing
Solution Approach 2:
The patent applies adaptive denoising that considers local image characteristics and channel-specific properties. By adjusting denoising strength and parameters locally for each channel based on its noise characteristics, the method achieves effective noise reduction while maintaining uniform filter behavior across channels and avoiding artifact amplification
3Reliability
If unmixing is performed prior to denoising, then noise law is preserved for denoising input, but the input image to denoiser does not follow the noise law anymore making noise estimation impossible
Solution Approach 1:
The patent inverts the conventional processing order by performing denoising before unmixing instead of after. This reversal allows the denoising algorithm to operate on the original Poisson-distributed noise characteristics of the raw fluorescence image, enabling effective noise estimation and removal while still achieving proper separation of overlapping dye signals through subsequent unmixing
Data Source
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AI summary
A first aspect of the present disclosure is related to a device for unmixing images of samples with fluorescent dyes, configured to: - obtain a mixed image of a sample; - determine an unmixed image based on the mixed image; - determine a noise-map image based on the mixed image; - determine a signal-to-noise image based on the unmixed image and the noise map-image ; - determine a denoised signal-to-noise image based on the signal-to-noise image ; - determine a noise-reduced unmixed image of the sample based the denoised signal-to-noise image and on a noise-map image .