Imaging Support Models for Automatic Lens Setting Optimization
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Solution Overview
Problem
Existing imaging systems face challenges in efficiently reducing the load associated with manual adjustment and optimization of imaging settings, particularly in lens-interchangeable and multi-system environments, leading to suboptimal image capture and processing.
Innovation Solution
An imaging support apparatus and method that utilizes a processor to generate and apply trained models based on captured images and associated settings, enabling adaptive control and optimization of imaging parameters through learning processing, including the generation of second trained models for different lenses and systems, and facilitating seamless transitions and adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment and optimization of imaging settings is performed, then imaging quality can be controlled, but operational load and time increase
Solution Approach 1:
The imaging apparatus automatically adjusts imaging settings by executing learned models that predict optimal parameters based on captured images, eliminating the need for manual user intervention in setting adjustment while maintaining high imaging quality
Solution Approach 2:
The system captures images, evaluates their quality using learned models, and automatically adjusts settings based on the evaluation results, creating a closed-loop feedback system that continuously optimizes imaging parameters without manual input
2Adaptability or versatility
If trained models are generated for different lenses and systems, then adaptability improves, but device complexity increases
Solution Approach 1:
A single learned model is designed to handle multiple imaging systems and lenses by capturing general patterns in image quality, allowing the model to evaluate and optimize settings across different camera systems without requiring separate specialized models for each
Solution Approach 2:
The learned model is divided into multiple sub-models, each specialized for evaluating specific lens characteristics or imaging conditions, allowing the system to manage complexity by organizing knowledge into modular, reusable units that can be selectively applied
Data Source
AI summary
There is provided an imaging support apparatus including a processor, and a memory in which the memory stores a first trained model, the first trained model is a trained model used for control related to imaging performed by an imaging apparatus, and the processor is configured to generate a second trained model used for the control by performing learning processing in which a first image, which is acquired by being captured by the imaging apparatus, and a set value, which is applied to the imaging apparatus in a case where the first image is acquired, are used as teacher data, and perform specific processing based on a first set value.