Editing Color balance (section)

==Illuminant estimation and adaptation==
[[File:Clifton Beach 5.jpg|thumb|right|300px|A seascape photograph at [[Clifton Beach, Tasmania|Clifton Beach]], [[South Arm, Tasmania|South Arm]], [[Tasmania]], Australia. The white balance has been adjusted towards the warm side for creative effect.]]
[[File:ColorChecker100423.jpg|thumb|right|300px|Photograph of a [[ColorChecker]] as a reference shot for color balance adjustments.]]
[[File:Government Center Miami color balance comparison.jpg|thumb|right|300px|Two photos of a high-rise building shot within a minute of each other with an entry-level point-and-shoot camera. Left photo shows a "normal", more accurate color balance, while the right side shows a "vivid" color balance, in-camera effects and no post-production besides black background.]]
[[File:PIA16800-MarsCuriosityRover-MtSharp-ColorVersions-20120823.jpg|thumb|right|300px|Comparison of color versions (raw, natural, white balance) of [[Mount Sharp|Mount Sharp (Aeolis Mons)]] on [[Mars]]]]
[[File:PIA16068 - Mars Curiosity Rover - Aeolis Mons - 20120817.jpg|thumb|right|300px|A white-balanced image of Mount Sharp (Aeolis Mons) on Mars]]
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.<ref>{{Cite book|last1=Afifi|first1=Mahmoud|last2=Price|first2=Brian|last3=Cohen|first3=Scott|last4=Brown|first4=Michael S|title=2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |chapter=When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images |date=2019|chapter-url=http://openaccess.thecvf.com/content_CVPR_2019/papers/Afifi_When_Color_Constancy_Goes_Wrong_Correcting_Improperly_White-Balanced_Images_CVPR_2019_paper.pdf|pages=1535–1544|doi=10.1109/cvpr.2019.00163|isbn=978-1-7281-3293-8|s2cid=196195956}}</ref> 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]]<ref name="Funt1996">Brian Funt, Vlad Cardei, and Kobus Barnard, "[http://www.cs.sfu.ca/~colour/publications/ARIZONA/arizona_abs.html Learning color constancy]", in ''Proceedings of the Fourth IS&T/SID Color Imaging Conference'', pp. 58–60 (1996).</ref> or a [[Bayesian method]].<ref name=Finlayson2001>{{Cite journal
 |author1=Graham Finlayson |author2=Paul M. Hubel |author3=Steven Hordley |date=November 2001
 | title = Color by correlation: a simple, unifying framework for color constancy
 | journal = [[IEEE Transactions on Pattern Analysis and Machine Intelligence]]
 | volume = 23
 | issue = 11
 | pages = 1209–21
 | doi = 10.1109/34.969113
 | url = http://www2.cmp.uea.ac.uk/Research/compvis/Papers/FinHorHub_PAMI01.pdf
|citeseerx=10.1.1.133.2101 }}</ref>