Adaptive Image Deconvolution for Noise-Resistant Microscopy
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Solution Overview
Problem
Existing iterative deconvolution methods in fluorescence microscopy often result in artifact-laden images due to the impact of noise, which distorts the image content and degrades quality.
Innovation Solution
A method and device for iterative deconvolution that generates two statistically independent images from the same sample area, determines relations between output estimates, checks deviations, and adapts the deconvolution rule based on a threshold to minimize noise influence, thereby reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative deconvolution methods are used to reconstruct object functions from images, then image resolution and quality are improved, but noise components are amplified and artifacts appear in the processed images
Solution Approach 1:
The patent divides the image processing into multiple iterations, where each iteration refines the object function estimate. The deconvolution process is segmented into successive approximation steps, allowing gradual improvement of image resolution while controlling noise amplification through iterative refinement rather than single-step processing.
Solution Approach 2:
The patent implements feedback mechanisms by using the reconstructed object function from each iteration to inform and adjust the next iteration's deconvolution process. The algorithm continuously compares the reconstructed image with the original image and adjusts the object function estimate accordingly, creating a feedback loop that improves resolution while monitoring and controlling artifact formation.
2Loss of information
If deconvolution is performed to reverse the convolution process and reconstruct the object function, then frequency components are recovered, but noise components at frequencies with low modulation are amplified
Solution Approach 1:
The patent applies dynamic adjustment of the object function estimate through iterative processing. Instead of a static deconvolution approach, the algorithm dynamically refines the object function across multiple iterations, adapting the reconstruction process to recover frequency components while progressively suppressing noise amplification through successive approximations.
Solution Approach 2:
The patent performs deconvolution to a controlled extent by limiting the number of iterations and using convergence criteria. Rather than attempting complete reversal of the convolution process which would maximize noise amplification, the method applies partial deconvolution that recovers sufficient frequency information while stopping before excessive noise amplification occurs.
Data Source
AI summary
The invention relates to the light microscopic acquisition of image data and the generation of deconvolved images. The deconvolution of an image is controlled by performing deconvolution steps in parallel on two control images, the results of which are compared with each other. Depending on the result of the comparison, the deconvolution rule for the actual image to be deconvolved is adapted. Other aspects relate in particular to the consideration of background and unwanted emissions from the sample during deconvolution.