Learning Model for Automatic Camera Color Matching
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Solution Overview
Problem
Matching development parameters between different camera models is challenging due to variations in sensor output and development processes, requiring manual adjustments and specialized knowledge, especially when trying to match colors between images from different camera makers or models.
Innovation Solution
An image processing apparatus that uses a learning model to acquire and estimate development parameters, allowing for automatic matching of image characteristics between cameras by generating a correspondence relationship between RAW image data parameters and developed image characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of development parameters is performed to match colors between different camera models, then color matching accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent creates a learning model that copies and stores the correspondence relationships between development parameters and image characteristics from multiple camera models. This allows the system to automatically determine appropriate parameters for matching images from different cameras without requiring manual adjustment, thus achieving accurate color matching while reducing time consumption.
Solution Approach 2:
The patent replaces the manual mechanical adjustment process with an automated learning model-based system. The learning model automatically determines development parameters by analyzing the correspondence relationships between parameters and image characteristics, eliminating the need for manual intervention and significantly reducing the time and effort required for color matching.
2Measurement precision
If specialized knowledge and dedicated measuring instruments are used to create 3DLUT for color matching, then color matching accuracy is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent creates a learning model that copies and stores the correspondence relationships between development parameters and image characteristics from multiple camera models. This allows the system to automatically determine appropriate parameters for matching images from different cameras without requiring manual adjustment, thus achieving accurate color matching while reducing time consumption.
Solution Approach 2:
The learning model acts as an intermediary that bridges the gap between different camera models. Instead of requiring direct manual adjustment or specialized measuring instruments, the learning model mediates the parameter transformation process, automatically translating development parameters from one camera model to another while maintaining color matching accuracy.
3Ease of operation
If development parameters are standardized across camera models, then ease of operation is improved, but adaptability to different camera characteristics deteriorates
Solution Approach 1:
The patent implements a dynamic parameter selection system that automatically adjusts development parameters based on the specific camera model and image characteristics. The learning model enables the system to adapt parameters in real-time according to the input image and camera type, maintaining both ease of operation through automation and high adaptability to different camera characteristics.
Solution Approach 2:
The patent dynamically changes development parameters based on the specific camera model and image characteristics. The learning model stores correspondence relationships between parameters and image characteristics, allowing the system to automatically select and adjust parameters to match the specific camera being used, thus maintaining adaptability while simplifying user operation.
Data Source
AI summary
An apparatus acquires RAW image data captured by a first device and captured image data captured by a second device, obtains a parameter of the first device by using a learning model that has learned a correspondence relationship between a parameter used to develop RAW image data and an image developed using the parameter such that the characteristics of a developed image of the RAW image data are close to the characteristics of the captured image, and develops the RAW image data.