In the field of computer vision, accurately perceiving and interpreting colors is a fundamental task. However, images captured under varying lighting conditions can alter the perceived colors, leading to challenges in maintaining color consistency. One popular approach to tackle this issue is the Gray-World Assumption. This article provides a overview of the Gray-World Assumption, its underlying principles and limitations in computer vision.
The Gray-World Assumption is a color constancy theory used in image processing to compensate for varying lighting conditions. Proposed in the context of image normalization, it assumes that the average reflectance of a scene is achromatic (gray). Simply put, this assumption states that if we average the colors in an image, they should ideally blend to a neutral gray. By using this principle, we can adjust the image colors to achieve a consistent appearance.
In mathematical terms, the Gray-World Assumption posits that the average color values across all pixels should be equal in the red, green, and blue (RGB) channels. If this isn’t the case, then the image colors are likely skewed by a lighting imbalance, and the colors can be shifted to correct this.
