Deep Generative Model Image Correction via Physics Regularization
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Solution Overview
Problem
Existing image restoration techniques face challenges in balancing reconstruction quality and algorithm complexity, often requiring manual prior model crafting and suffer from limited availability of training data and poor generalization, especially in imaging systems like medical scanners.
Innovation Solution
A deep-learnt generative model is integrated as a regularizer with a physics model of the degradation behavior to correct images, allowing for automated artifact minimization without the need for application-specific balancing, using a probability-based approach to restore images from corrupted measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual prior model crafting is used for image restoration, then algorithm complexity is reduced, but reconstruction quality is compromised due to simplistic assumptions
Solution Approach 1:
The patent replaces manual prior model crafting (mechanical/systematic approach) with a deep generative machine-learning model (data-driven approach). The neural network automatically learns complex image priors from training data, eliminating the need for manual assumption-based model design while achieving superior reconstruction quality.
Solution Approach 2:
The patent transforms the rigid, hand-crafted prior model parameters into flexible, learned parameters within a deep neural network. The model adapts its internal parameters through training on large datasets, enabling it to capture complex image structures without manual intervention, thus resolving the trade-off between complexity and quality.
2Manufacturing precision
If supervised machine learning is used for image correction, then reconstruction quality improves, but the requirement for large sets of distortion and imaging system-specific training data increases complexity and limits availability
Solution Approach 1:
The patent creates a universal deep generative model that can handle multiple types of image distortions and imaging systems through a single framework. The model is trained on diverse data to learn generalizable image priors, enabling it to correct various artifacts (noise, blur, motion) across different imaging modalities without requiring separate models for each specific distortion type.
Solution Approach 2:
The patent performs preliminary training of the deep generative model on large, diverse datasets of clean images before deployment. This pre-training establishes a robust prior understanding of image structures that can be applied to various correction tasks, reducing the need for task-specific data collection and enabling effective correction with limited problem-specific training data.
3Manufacturing precision
If supervised machine learning is used for image correction, then reconstruction quality improves, but generalization performance deteriorates when distortions differ from training data
Solution Approach 1:
The patent employs a deep generative model with learnable parameters that adapt to different distortion types through the physics model. The model's flexibility allows it to generalize to unseen distortions by combining its learned image priors with the specific degradation characteristics of the imaging system, rather than requiring exact matches to training data.
Solution Approach 2:
The patent introduces a physics model of degradation behavior as an intermediary between the deep generative model and the corrupted image. This physics model acts as a bridge that translates the model's general image priors into specific correction strategies for different distortion types, enabling the system to generalize to new distortion scenarios without retraining.
4Manufacturing precision
If application-specific balancing is performed for prior model and algorithm, then reconstruction quality optimizes for specific cases, but device complexity and requirement for application-specific tuning increase
Solution Approach 1:
The patent enables the deep generative model to automatically adapt to different applications and imaging systems through self-service mechanisms. The model learns robust image priors during training that are applicable across multiple scenarios, and the physics model automatically adjusts the correction process based on the specific degradation characteristics, eliminating the need for manual application-specific balancing.
Data Source
AI summary
For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.