Multi-camera color balancing using reference image statistics
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Solution Overview
Problem
Conventional digital cameras struggle to accurately reproduce scene colors due to the influence of varying light sources, as they lack adaptive capabilities similar to the human visual system, leading to impractical and inaccurate color balancing methods such as using light meters or gray cards.
Innovation Solution
Employing multiple cameras, where one camera captures a reference image of the photographer or a known surface, allowing for estimation of the light source's color and subsequent application of color correction factors to balance the primary image, thereby enabling convenient and accurate color balancing without additional equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dedicated light meter is equipped in each camera, then color balancing accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The second camera, originally designed for capturing photographer images or other scenes, is repurposed to serve dual functions: it continues to capture its intended subject while simultaneously providing image data for color balancing the first camera. This eliminates the need for a dedicated light meter in each camera while maintaining color balancing capability.
Solution Approach 2:
The system uses the second camera's own image data to generate color correction information that benefits the first camera. The second camera essentially serves itself by providing reference data that enables both cameras to achieve accurate color balancing without external dedicated equipment.
2Measurement precision
If a gray card is used for color balancing, then color balancing accuracy is improved, but ease of operation deteriorates due to additional setup requirements
Solution Approach 1:
Instead of requiring the photographer to manually set up a gray card, the system automatically uses the second camera to capture an image of the photographer or surrounding environment. The processor then automatically extracts color balancing information from this image, eliminating manual setup steps while maintaining accuracy.
Solution Approach 2:
The second camera captures the reference image (photographer or environment) simultaneously with or before the first camera captures the main scene. This preliminary capture of reference data allows the processor to automatically determine color correction factors before final image processing, eliminating the need for separate gray card setup steps.
3Ease of operation
If statistics-based computational approach is used, then ease of operation is improved, but color balancing accuracy deteriorates in many scenarios
Solution Approach 1:
The second camera acts as an intermediary that captures a controlled reference scene (photographer or environment) under the same lighting conditions as the main scene. This reference image serves as a mediator between the unknown light source characteristics and the main image, enabling accurate statistical analysis without requiring complex manual calibration.
Solution Approach 2:
The system changes the parameter being analyzed by using skin tone detection or environmental reference objects with known color properties. Instead of relying on general scene statistics that may be inaccurate, the system specifically targets parameters related to human skin or known reference objects, improving the reliability of color balancing calculations.
Data Source
AI summary
Pictures can be taken with multiple (e.g., two) cameras, and the statistics associated with any of those pictures can be used to correct (e.g., color balance) any of the other pictures. Generally speaking, first image data captured by a first camera is accessed (e.g., retrieved from memory). Similarly, second image data captured by a second camera is accessed. The first image data and second image data are acquired at or about the same time using the first and second cameras together (e.g., at the same location, so that each camera is subject to the same light source). The first image data can then be processed (e.g., color balanced) using information that is derived using the second image data.