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Volume: 14 | Article ID: art00021
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Automatic Compensation for Camera Settings for Images Taken under Different Illuminants
  DOI :  10.2352/CIC.2006.14.1.art00021  Published OnlineJanuary 2006
Abstract

The combination of two images shot for the same scene but under different illumination has been used in wide applications ranging from estimating scene illumination, to enhancing photographs shot in dark environments, to shadow removal. An example is the use of a pair of images shot with and without a flash. However, for consumer-grade digital cameras, due to the different illumination conditions the two images usually have different camera settings when they are taken, such as exposure time and white balance. Thus adjusting (registering) the two images becomes a necessary step prior to combining the two images. Unfortunately, how to register these two images has not been investigated fully. In this paper, we propose a method which can parametrically adjust the two images so as to compensate for the difference in exposure speed, ISO, aperture size, and white balance. This is accomplished by training a 2nd-order masking model on a set of image pairs to predict the model parameters. This trained model can then be used to register two images. In the training phase, we wish to develop a scheme for adjusting the magnitude in each color channel of one image to register with the other image, for each image pair. The problem is difficult because the difference between the two images is a composite of both camera settings and illumination. Here, we use the simple fact that a shadow effect should be caused purely by the changes of illumination. Suppose we have two images, one of which is taken under illuminant 1 and the other is taken under illuminant 1 plus illuminant 2. If we subtract the first image from the second, a shadow caused by illuminant 1 should disappear in the resulting difference. By adjusting the RGB pixel values of one image so as to completely remove the shadow in the difference image, compensating magnitudes for each color channel can be computed and used to train a masking model. This masking model can then accurately compensate for camera settings for any two new images such that the difference between compensated images reflects only the difference in illumination.

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  Cite this article 

Cheng Lu, Mark S. Drew, "Automatic Compensation for Camera Settings for Images Taken under Different Illuminantsin Proc. IS&T 14th Color and Imaging Conf.,  2006,  pp 114 - 118,  https://doi.org/10.2352/CIC.2006.14.1.art00021

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