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A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast
Edoardo Provenzi1, Carlo Gatta, Massimo Fierro
1Dipartimento di Tecnologies dell'Informazione, Università degli Studi di Milano, Crema, Italy. provenzi@dti.unimi.it
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 16, 2008
Summary
A novel color correction model, RACE, combines Random Spray Retinex (RSR) and Automatic Color Equalization (ACE) for improved performance. A new regulation mechanism addresses common drawbacks in differential color correction models.
Area of Science:
- Computer Vision
- Image Processing
- Color Science
Background:
- Perceptually inspired color correction models like Retinex are crucial for image reproduction and feature recognition.
- Existing models, including white-patch (WP) and gray-world (GW) algorithms, have distinct characteristics and limitations.
- Differential, spatially-variant, and non-linear approaches are common in these color correction techniques.
Purpose of the Study:
- To develop a more robust and higher-performing color correction model by integrating WP and GW algorithms.
- To address the common drawback of uniform image area corruption found in differential color correction models.
- To introduce a generalizable regulation mechanism applicable to various perceptually inspired color correction algorithms.
Main Methods:
- A unified spatially-variant approach was identified for Random Spray Retinex (RSR) and Automatic Color Equalization (ACE).
- The distinct non-linear and differential mechanisms of RSR and ACE were mathematically fused using spray techniques and local averaging.
- A local and global contrast-based, image-driven regulation mechanism was devised to overcome image corruption in uniform areas.
Main Results:
- The combined model, RACE (Random Spray Retinex and Automatic Color Equalization), demonstrated enhanced robustness and performance compared to individual algorithms.
- A significant drawback of differential models, the corruption of uniform image areas, was identified and addressed.
- The proposed regulation mechanism proved effective in mitigating artifacts and improving overall color correction quality.
Conclusions:
- The RACE model offers a superior approach to color correction by synergistically combining WP and GW algorithms.
- The developed regulation mechanism provides a general solution for improving perceptually inspired color correction techniques.
- This research advances the field of image processing by offering a more robust and artifact-free color correction solution.
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