Optical Image Noise Removal Using Photodetector-Specific AI Models
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Solution Overview
Problem
Existing noise removal methods in optical imaging using machine learning are ineffective due to fluctuating luminance-noise relationships based on imaging conditions, particularly the type of photodetector used.
Innovation Solution
An optical image processing system that selects a trained model from a plurality of models built through machine learning based on imaging information, using a noise distribution model to generate training images and perform noise removal tailored to the imaging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single trained model is used for noise removal, then the device complexity is reduced, but the noise removal effectiveness deteriorates due to fluctuating luminance-noise relationships under different imaging conditions
Solution Approach 1:
The system dynamically selects from multiple trained models based on imaging conditions (photodetector type, imaging parameters). This dynamic adaptation allows the noise removal process to adjust to varying luminance-noise relationships without permanently increasing system complexity, resolving the contradiction between using multiple models and maintaining simplicity
Solution Approach 2:
The system changes the parameter of model selection based on imaging conditions. By detecting photodetector type and imaging parameters, the system selects the appropriate trained model, ensuring effective noise removal across different conditions without requiring all models to be active simultaneously, thus maintaining device simplicity while improving reliability
2Reliability
If multiple trained models are built for different imaging conditions, then the noise removal effectiveness is improved, but the device complexity increases
Solution Approach 1:
Multiple trained models are built in advance for different imaging conditions and stored for later use. This preliminary preparation ensures that when imaging occurs, the appropriate model is already available, improving noise removal effectiveness without requiring real-time model generation or complex runtime decisions about model creation
Solution Approach 2:
The system achieves multi-functionality by having a single noise removal system that can handle multiple imaging conditions through model selection. Rather than creating separate dedicated systems for each condition, one universal system selects from multiple models, improving effectiveness while controlling complexity through shared infrastructure
3Adaptability or versatility
If a trained model is selected based on imaging information, then the adaptability to different imaging conditions is improved, but the processing time increases due to model selection
Solution Approach 1:
Imaging information is acquired and analyzed in advance to determine which trained model to use before actual noise removal processing begins. This preliminary model selection based on pre-acquired imaging information minimizes time loss during the critical noise removal phase while maintaining adaptability to different conditions
Data Source
AI summary
An optical image processing module includes an image acquisition unit that acquires an optical image obtained by capturing an image of light from a target object, a selection unit that selects a trained model from a plurality of trained models each of which is built in advance through machine learning using condition information including photodetector information indicating a type of photodetector, and a processing unit that inputs the optical image to the selected trained model and executes image processing of removing noise from the optical image.