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Automatic Compensation for Camera Settings for Images Taken under Different Illuminants

机译:自动补偿在不同光源下拍摄的图像的相机设置

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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 para-metrically 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 illumi-nant 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.
机译:针对同一场景但在不同照明条件下拍摄的两个图像的组合已被广泛用于从估计场景照明到增强在黑暗环境下拍摄的照片到去除阴影的应用中。一个示例是使用一对带有和不带有闪光灯的图像。但是,对于消费级数码相机,由于照明条件不同,两个图像在拍摄时通常具有不同的相机设置,例如曝光时间和白平衡。因此,调整(配准)两个图像成为组合两个图像之前的必要步骤。不幸的是,如何注册这两个图像尚未得到充分研究。在本文中,我们提出了一种可以参数调整两个图像的方法,以补偿曝光速度,ISO,光圈大小和白平衡方面的差异。这是通过在一组图像对上训练一个二级掩盖模型来预测模型参数来实现的。然后,可以使用该训练的模型来注册两个图像。在训练阶段,我们希望为每个图像对开发一种方案,用于调整一个图像的每个颜色通道中的大小,以与另一幅图像对齐。由于两个图像之间的差异是相机设置和照明的综合,因此该问题很难解决。在这里,我们使用一个简单的事实,即阴影效应应完全由照明的变化引起。假设我们有两个图像,其中一个是在光源1下拍摄的,另一个是在光源1加照度2下拍摄的。如果我们从第二个图像中减去第一个图像,则由光源1引起的阴影应消失,从而导致差异。通过调整一个图像的RGB像素值,以完全消除差异图像中的阴影,可以计算每个颜色通道的补偿幅度并将其用于训练蒙版模型。然后,此遮罩模型可以为任何两个新图像准确补偿相机设置,以使补偿后图像之间的差异仅反映照明差异。

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