page_title: Color balance
section_title: Illuminant estimation and adaptation
hierarchical_section_title: Color balance / Illuminant estimation and adaptation
caption_attribution_description: English: 08.17.2012 The Promised Land This image (cut out from a mosaic) shows the view from the landing site of NASA's Curiosity rover toward the lower reaches of Mount Sharp, where Curiosity is likely to begin its ascent through hundreds of feet (meters) of layered deposits. The lower several hundred feet (meters) show evidence of bearing hydrated minerals, based on orbiter observations. The terrain Curiosity will explore is marked by hills, buttes, mesas and canyons on the scale of one-to-three story buildings, very much like the Four Corners region of the western United States. A scale bar indicates a distance of 1.2 miles (2 kilometers). Curiosity's 34-millimeter Mast Camera acquired this high-resolution image on Aug. 8, 2012 PDT (Aug. 9 EDT). This image shows the colors modified as if the scene were transported to Earth and illuminated by terrestrial sunlight. This processing, called "white balancing," is useful to scientists for recognizing and distinguishing rocks by color in more familiar lighting. Image Credit: NASA/JPL-Caltech/MSSS
context_page_description: In photography and image processing, color balance is the global adjustment of the intensities of the colors. An important goal of this adjustment is to render specific colors – particularly neutral colors – correctly. Hence, the general method is sometimes called gray balance, neutral balance, or white balance. Color balance changes the overall mixture of colors in an image and is used for color correction. Generalized versions of color balance are used to correct colors other than neutrals or to deliberately change them for effect.
  Image data acquired by sensors – either film or electronic image sensors – must be transformed from the acquired values to new values that are appropriate for color reproduction or display. Several aspects of the acquisition and display process make such color correction essential – including that the acquisition sensors do not match the sensors in the human eye, that the properties of the display medium must be accounted for, and that the ambient viewing conditions of the acquisition differ from the display viewing conditions.
context_section_description: Most digital cameras have means to select color correction based on the type of scene lighting, using either manual lighting selection, automatic white balance, or custom white balance. The algorithms for these processes perform generalized chromatic adaptation.
  Many methods exist for color balancing.  Setting a button on a camera is a way for the user to indicate to the processor the nature of the scene lighting. Another option on some cameras is a button which one may press when the camera is pointed at a gray card or other neutral colored object. This captures an image of the ambient light, which enables a digital camera to set the correct color balance for that light.
  There is a large literature on how one might estimate the ambient lighting from the camera data and then use this information to transform the image data. A variety of algorithms have been proposed, and the quality of these has been debated. A few examples and examination of the references therein will lead the reader to many others. Examples are Retinex, an artificial neural network or a Bayesian method.
caption: A white-balanced image of Mount Sharp (Aeolis Mons) on Mars
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