White balancing is the process of removing unrealistic color cast from an image caused by the color temperature of the illumination source. Different illumination sources such as daylight, incandescent light, and fluorescent light have different power spectral distributions that can cause distortions in the colors of an image. Such distortions can, for example, result in a white object not appearing white under the color cast of the illumination source. For example, a low color temperature (for example, 2000K) illumination source may give an image a reddish cast, whereas a high color temperature (for example, 9000K) illumination source may give an image a bluish cast. Whereas human eyes can automatically adapt to the temperature color of the illumination, currently-available (or conventional) artificial image acquisition devices and methods (for example, image sensors used in cameras and camcorders) cannot adapt automatically and thus produce certain color cast artifacts because of the color temperature of the illumination source. By compensating for illumination through white balancing, resulting images have more realistic colors.
Commercially available digital cameras have white balancing functions that, for example, allow a user to choose a color temperature setting from a collection of pre-defined illumination settings (for example, incandescent, fluorescent, cloudy, flash, sunny, candlelight, etc.). Using a predefined illumination setting, however, can produce suboptimal results if actual lighting conditions do not match the illumination setting. Alternatively, various techniques are available to perform automatic white balancing (AWB)—that is, without user specification of the illumination setting. Such AWB techniques are necessarily based on the color composition of the image. It can be difficult and computationally intensive, however, to perform AWB without an external reference regarding the color temperature of the illumination source. Errors in AWB techniques can easily occur when the technique is unable to distinguish between an overall color cast caused by the illumination source versus the intrinsic color bias of the composition of the scene.
For example, the commonly-used AWB technique known as “gray world” assumes that the average color in a scene is gray or colorless. The gray world technique can be effective when the scene contains a multitude of colors that average out to gray. But, the gray world technique can introduce significant color bias when the average color of a scene is not gray. This problem is particularly acute in non-gray monochromatic scenes (for example, scenes of a blue ocean or green foliage). Another AWB technique, the “max-RGB” method, assumes that the combination of maxima obtained from each of the three color channels red, green, and blue is the color of the illumination source. The max-RGB method is more effective than the gray world method for monochromatic scenes, but the effectiveness of the max-RGB method is highly dependent on the scene composition. Other white balancing algorithms have similar limitations. Furthermore, some algorithms that have high computational complexity (for example, because they require a large number of differential operations) are unsuitable for practical use, particularly for real-time video processing.
In view of the foregoing, there is a need for improved white balancing systems and methods that perform accurate white balancing for various types of image compositions while being computationally efficient.
It should be noted that the figures are not drawn to scale and that elements of similar structures or functions are generally represented by like reference numerals for illustrative purposes throughout the figures. It also should be noted that the figures are only intended to facilitate the description of the preferred embodiments. The figures do not illustrate every aspect of the described embodiments and do not limit the scope of the present disclosure.
USPTO Backgrounds
USPTO Backgrounds