Probabilistic Illumination Color Correction for Anomalous Imaging Devices
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Solution Overview
Problem
Existing color correction methods for image data are limited by the inability of imaging devices to adapt spectral responses to varying lighting conditions and inadequate information about the viewing conditions, leading to ineffective color balancing.
Innovation Solution
A system that determines illumination probability information by analyzing image data using a combination of hardware and software components to calculate illumination color and probability distributions, employing techniques like principal component analysis (PCA) and gray-world assumptions to transform and normalize color data, allowing for accurate color correction across different lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If chromatic adaptation models based on Von Kries hypothesis are used for color correction, then color transformation can be achieved with simple diagonal transform using three gains, but the model may not hold true for given imaging device with anomalous spectral responses
Solution Approach 1:
The patent changes the parameters of the chromatic adaptation model by introducing device-specific spectral response characteristics and viewing condition parameters. Instead of using fixed Von Kries gains, the system adapts the model parameters to match the specific imaging device's spectral responses and the actual viewing conditions, thereby maintaining both simplicity and accuracy.
Solution Approach 2:
The patent makes the color correction model dynamic by allowing it to adapt to different viewing conditions and device characteristics. The system dynamically adjusts the adaptation parameters based on the specific imaging device being used and the actual lighting conditions, rather than relying on static assumptions.
2Ease of operation
If existing chromatic adaptation models are used, then color transformation can be performed with limited information, but inadequate information about viewing conditions limits the effectiveness of color correction
Solution Approach 1:
The patent performs preliminary characterization of the imaging device's spectral responses and establishes a database of viewing condition parameters before actual color correction is needed. This preliminary action allows the system to quickly apply pre-computed adaptation parameters when color correction is required, maintaining ease of operation while improving precision.
Solution Approach 2:
The patent introduces an intermediary layer of device-specific adaptation parameters that mediate between the generic chromatic adaptation model and the actual device characteristics. This intermediary allows the system to work with limited viewing condition information while still achieving accurate color correction by compensating for device-specific anomalies.
3Ease of manufacture
If imaging devices with fixed spectral responses are used, then device design is simplified, but the devices cannot adapt to different lighting conditions
Solution Approach 1:
The patent enables the imaging device to perform self-characterization by capturing images under known reference illuminations and automatically determining its own spectral response parameters. This self-service approach allows devices with fixed spectral responses to achieve adaptability through software-based characterization without requiring complex hardware modifications.
Solution Approach 2:
The patent compensates for fixed spectral responses by dynamically changing the software parameters used in color correction. Instead of modifying the physical spectral responses of the device, the system adjusts the adaptation parameters and transformation matrices to account for the fixed characteristics, thereby achieving adaptability through parameter manipulation.
Data Source
AI summary
Methods and systems to determine a probability that a given illumination is a reference illumination. In an embodiment, data representing a set of respective captures of reference targets under a reference illumination may be used to generate a probability distribution for the reference illumination. In another embodiment, one or more such probability distributions, each corresponding to a respective reference illumination, may be used in estimating a non-reference illumination.