Imaging Apparatus Data Generation for Machine Learning Color Standardization
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Solution Overview
Problem
Variations in image quality due to different image processing parameters across various imaging apparatuses result in inconsistent image data, affecting machine learning applications where standardized image quality is crucial.
Innovation Solution
A data generation method and imaging apparatus that perform first image processing on RAW data and generate accessory information based on image processing details, allowing for correction of image data to approximate a reference color standard, thereby reducing variations in image quality across different imaging devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image processing is performed using different parameters across various imaging apparatuses, then each apparatus can optimize image quality for its specific characteristics, but image quality becomes inconsistent and varies between different imaging devices
Solution Approach 1:
The patent applies parameter changes by introducing a correction parameter that transforms image data from different imaging apparatuses to a unified color standard. Instead of using fixed, apparatus-specific processing parameters, the system dynamically adjusts parameters based on the original imaging apparatus type, enabling both optimization for each device and consistency across all devices through parameter transformation.
Solution Approach 2:
The patent uses an intermediary approach by introducing a correction parameter as a mediator between the original image data and the final standardized output. This correction parameter acts as a transformation bridge that reconciles the differences between various imaging apparatuses, allowing each apparatus's optimized data to be converted into a unified standard without losing the benefits of device-specific optimization.
2Manufacturing precision
If image data is standardized to a reference color, then image quality consistency across different imaging apparatuses is improved, but the complexity of processing increases due to additional correction steps
Solution Approach 1:
The patent applies preliminary action by determining the correction parameter before the actual image processing is completed. The system identifies the imaging apparatus type and retrieves the corresponding correction parameter in advance, so that when image data needs to be standardized, the correction can be applied directly without adding complex real-time processing steps. This pre-preparation simplifies the overall processing workflow.
3Measurement precision
If accessory information including image processing details is generated, then image data can be corrected to approximate reference color, but data generation complexity increases
Solution Approach 1:
The patent merges the generation of accessory information with the existing image processing workflow. Instead of creating a separate, complex data generation system, the correction parameter is integrated into the processing pipeline, and accessory information is generated as part of the standard processing steps. This merging approach maintains high color accuracy while avoiding additional complexity by combining multiple functions into a unified process.
Data Source
AI summary
There is a data generation method of generating first image data which is image data obtained by imaging a subject via an imaging apparatus and used in machine learning and which includes accessory information. The data generation method includes a first generation step of generating the first image data by performing first image processing via the imaging apparatus, and a second generation step of generating first information based on image processing information related to the first image processing, as information included in the accessory information.