Microscope Image Processing for Mixed Deconvolution and Denoising
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Solution Overview
Problem
Conventional image processing systems in microscopes are limited to deconvolution and denoising algorithms that require oversampling, making them ineffective in real-time imaging with varying sampling densities, and there is a need for a system that can improve image quality across a wide range of sampling densities.
Innovation Solution
An image processing system that performs joint deconvolution and denoising in real-time, adjusting the weighting of these processes based on the sampling density to ensure effective image enhancement regardless of sampling conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deconvolution algorithms are applied to improve spatial resolution, then image quality is improved under oversampling conditions, but the system becomes inapplicable under undersampling conditions which limits versatility
Solution Approach 1:
The system dynamically adjusts the processing mode between deconvolution and denoising based on the detected sampling density. When oversampling is detected, deconvolution is applied to enhance spatial resolution; when undersampling is detected, denoising is applied instead. This dynamic adaptation resolves the contradiction by making the system versatile across different sampling conditions while maintaining optimal image quality for each condition.
Solution Approach 2:
The system changes the processing parameter (processing mode) based on the sampling density parameter. By detecting whether the image is oversampled or undersampled, the system switches between deconvolution and denoising algorithms, thereby adapting to different sampling conditions and resolving the limitation of deconvolution being applicable only to oversampled images.
2Measurement precision
If deconvolution is applied to enhance spatial resolution, then image quality improves for oversampled images, but artefacts occur in undersampled images which reduces reliability
Solution Approach 1:
The system incorporates a feedback mechanism that detects the sampling density of the input image and uses this information to determine the appropriate processing mode. This feedback loop ensures that deconvolution is only applied when conditions are suitable (oversampling), preventing artefact generation and maintaining reliable image quality across different sampling conditions.
Solution Approach 2:
The processing mode is dynamically selected based on the sampling density assessment. The system transitions between deconvolution and denoising modes depending on whether the image is oversampled or undersampled, thereby maintaining reliability by avoiding deconvolution artefacts in unsuitable conditions while still achieving spatial resolution enhancement when appropriate.
3Measurement precision
If separate deconvolution and denoising processes are applied alternatively, then each process can be optimized for its specific function, but the processing complexity increases and real-time performance deteriorates
Solution Approach 1:
The system segments the processing decision into two distinct paths: deconvolution for oversampled images and denoising for undersampled images. By dividing the processing logic based on sampling density, the system avoids the complexity of applying both processes alternatively, thereby enabling real-time performance while maintaining optimized image quality for each specific condition.
Solution Approach 2:
The system dynamically selects between deconvolution and denoising based on real-time assessment of sampling density, rather than applying both processes alternatively. This dynamic selection reduces processing complexity and computational overhead, enabling real-time image enhancement while maintaining optimal quality for each sampling condition.
Data Source
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AI summary
An image processing system (106) comprises a processor (108). The processor (108) is configured to obtain image pixel data generated by an optical imaging system of a microscope (100). The processor (106) is configured to perform deconvolution processing on the obtained image pixel data for generating deconvolved image pixel data. The processor (106) is configured to perform denoising processing on the obtained image pixel data for generating denoised image pixel data. The processor (106) is configured to obtain a sampling density based on which the image pixel data is generated by the optical imaging system. The processor (106) is configured to mix the deconvolved image pixel data and the denoised image pixel data for generating mixed image pixel data with a weighting dependent on the sampling density to change - in particular to increase - a ratio of the deconvolved image pixel data in relation to the denoised image pixel data when the sampling density exceeds an oversampling limit (NL).