3D Image Recomposition Using White Top Hat Layer Scaling
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Solution Overview
Problem
Current imaging technologies fail to produce a highly defocused image in the image quality due to the presence of noise and background intensity variation, leading to a non-uniform illumination, which may be due to the system noise, stray light, non-uniform illumination, or emissions from defocused objects in the sample volume.
Innovation Solution
A morphology-based recomposition method using white top hat (WTH) transforms and scaling factors to generate orthogonalized layers from input images, which are then scaled to increase image resolution and reduce background interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D deconvolution is applied to each image in the stack, then image resolution is restored to some degree, but noise amplification and out-of-focus content increase
Solution Approach 1:
The patent segments the image restoration process into two distinct stages: first applying 2D deconvolution to restore resolution, then applying morphology-based recomposition to remove out-of-focus content and noise. This segmentation allows each method to address specific aspects of image quality without compounding their negative effects.
Solution Approach 2:
The patent introduces morphology-based recomposition as an intermediary processing step between 2D deconvolution and final image output. This intermediary process filters out the noise and out-of-focus content amplified by deconvolution while preserving the resolution improvements, acting as a mediator between the two conflicting requirements.
2Measurement precision
If Richardson-Lucy deconvolution algorithm is used, then image resolution is restored, but computation time increases and automation becomes difficult
Solution Approach 1:
The patent performs preliminary determination of the optimal number of iterations for the Richardson-Lucy algorithm based on image characteristics and PSF properties before actual processing. This preliminary action establishes fixed termination criteria that enable automated processing without user intervention while maintaining optimal resolution restoration.
Solution Approach 2:
The system implements self-service automation where the morphology-based recomposition automatically adjusts processing parameters and removes artifacts without requiring user intervention. The entire pipeline from deconvolution to final processing runs autonomously, eliminating the need for manual trial-and-error adjustments.
3Measurement precision
If correct PSF is used for deconvolution, then image quality is improved, but determining accurate PSF is time intensive
Solution Approach 1:
The patent employs computationally inexpensive methods to estimate the PSF that provide sufficient accuracy for practical purposes, rather than using time-intensive measurement techniques. The morphology-based recomposition is particularly effective when PSF is only approximately known, making accurate PSF determination optional rather than critical.
4Measurement precision
If morphology-based recomposition is applied, then background interference is reduced and resolution is increased, but processing complexity increases
Solution Approach 1:
The patent merges morphology-based recomposition with the deconvolution pipeline into a unified processing workflow. By combining these operations and optimizing their integration, the system achieves high image quality without proportionally increasing processing complexity. The two methods work synergistically rather than as separate, independent stages.
Data Source
AI summary
Methods and systems are provided herein for generating a morphology-based recomposition based on 3-dimensional input image data including an image stack of 2-dimensional (2D) images, the morphology-based recomposition being a final image generated by performing one or more white top hat (WTH) transforms on each 2D image, generating two or more image layers based on two or more WTH transformed images, and scaling adjacent image layers based on one or more scaling factors, each scaling factor being based on an estimated point spread function (PSF), two structure element sizes, and standard deviations of the estimated PSF and image data.