Super-Resolution Image Reconstruction With Physics-Guided AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for reconstructing super-resolution microscopic images often result in artifacts and unsatisfactory signal-noise ratios due to the lack of constraint by imaging principles in trained machine-learning models.
Innovation Solution
An image processing model incorporating a first sub-model for a likelihood term and a second sub-model as a trained machine-learning model to optimize an objective function, ensuring the reconstruction adheres to imaging principles and reduces artifacts and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a trained machine-learning model is used to reconstruct super-resolution images, then the reconstruction speed and automation are improved, but artifacts and errors occur due to lack of constraint by imaging principles
Solution Approach 1:
The image processing model is divided into two independent sub-models: a first sub-model that determines optimization terms based on imaging principles (likelihood term) and a second sub-model that is a trained machine-learning model (regularization term). This segmentation allows each sub-model to specialize in different aspects, with the first sub-model ensuring physical correctness and the second sub-model providing noise reduction, thereby resolving the contradiction between automation and accuracy.
Solution Approach 2:
The patent introduces an intermediary objective function that combines both the likelihood term (from imaging principles) and the regularization term (from machine-learning models). This intermediary framework mediates between the physical constraints of imaging and the computational power of machine-learning, allowing the system to benefit from both while avoiding the artifacts of using either alone.
2Reliability
If conventional reconstruction methods are used, then imaging principles are followed, but artifacts and poor signal-noise ratios result
Solution Approach 1:
The patent merges two previously separate approaches: conventional imaging-principle-based reconstruction methods and machine-learning-based denoising. The first sub-model ensures constraint by imaging principles while the second sub-model addresses artifacts and noise. By combining these approaches in a unified optimization framework, the system achieves both reliability and reduced harmful factors.
Solution Approach 2:
The image processing model functions as a composite system, integrating different methodological materials (imaging principles and machine-learning algorithms) into a single cohesive structure. The first sub-model provides the structural framework based on imaging physics, while the second sub-model adds the noise-reduction layer, creating a composite approach that overcomes the limitations of individual methods.
3Device complexity
If a single sub-model is used for image processing, then the model complexity is reduced, but the ability to handle both imaging principles and noise reduction is compromised
Solution Approach 1:
The model is segmented into two specialized sub-models: the first sub-model handles imaging principles and likelihood optimization, while the second sub-model handles noise reduction and regularization. This segmentation increases versatility by allowing each sub-model to specialize in specific tasks, while the overall structure remains manageable through modular design.
Solution Approach 2:
The dual sub-model structure provides multi-functionality within a single image processing system. The first sub-model universally applies imaging principles across different imaging modalities, while the second sub-model universally handles noise reduction tasks. This multi-functional design enhances adaptability without requiring separate systems for each function.
Data Source
AI summary
Methods and systems for image processing are provided. The method may include obtaining image data generated by an image acquisition device; generating a preliminary image by processing the image data; and generating a target image by using an image processing model to process the preliminary image according to an optimization algorithm. The image processing model includes a first sub-model and a second sub-model. The first sub-model is configured to determine a first optimization term related to a likelihood term of an objective function, and the second sub-model is configured to determine a second optimization term related to a regularization term of the objective function, wherein the second sub-model is a trained machine-learning model.