557 research outputs found
Deep White-Balance Editing
We introduce a deep learning approach to realistically edit an sRGB image's
white balance. Cameras capture sensor images that are rendered by their
integrated signal processor (ISP) to a standard RGB (sRGB) color space
encoding. The ISP rendering begins with a white-balance procedure that is used
to remove the color cast of the scene's illumination. The ISP then applies a
series of nonlinear color manipulations to enhance the visual quality of the
final sRGB image. Recent work by [3] showed that sRGB images that were rendered
with the incorrect white balance cannot be easily corrected due to the ISP's
nonlinear rendering. The work in [3] proposed a k-nearest neighbor (KNN)
solution based on tens of thousands of image pairs. We propose to solve this
problem with a deep neural network (DNN) architecture trained in an end-to-end
manner to learn the correct white balance. Our DNN maps an input image to two
additional white-balance settings corresponding to indoor and outdoor
illuminations. Our solution not only is more accurate than the KNN approach in
terms of correcting a wrong white-balance setting but also provides the user
the freedom to edit the white balance in the sRGB image to other illumination
settings.Comment: Accepted as Oral at CVPR 202
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