Color Alignment for Relative Color Constancy via Non-Standard References.

Area of Science:

• Computer Vision
• Image Processing
• Color Science

Background:

• Digital cameras exhibit varying image formations and inaccessible native sensor outputs, hindering consistent color assessment.
• This inconsistency negatively impacts the performance of computer vision algorithms in scientific imaging applications.

Purpose of the Study:

• To develop a robust color alignment model that addresses the challenges of device-specific image formation and inaccessible native sensor data.
• To enable consistent color assessment across diverse digital cameras, even with non-standard color references.

Main Methods:

• A three-step process: camera response calibration, response linearization, and color matching, treating camera image formation as a black box.
• Utilizes a novel balance-of-linear-distances feature for unsupervised camera parameter determination using non-standard color references.
• Requires a minimal number of corresponding color patches for effective color alignment.

Main Results:

• The proposed model demonstrated superior performance in color alignment compared to existing popular and state-of-the-art methods.
• Evaluated on three challenging datasets with varied illumination, exposure, and imaging conditions, including scientific imaging scenarios.
• Successfully achieved consistent color assessment across multiple cameras without prior knowledge of true color values.

Conclusions:

• The developed color alignment model effectively resolves inconsistencies in digital camera color output.
• Offers a practical solution for scientific imaging applications requiring reliable color assessment across different devices.
• The unsupervised approach and minimal data requirements make it a versatile tool for computer vision tasks.