7 research outputs found
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Computational color constancy is a preprocessing step used in many camera
systems. The main aim is to discount the effect of the illumination on the
colors in the scene and restore the original colors of the objects. Recently,
several deep learning-based approaches have been proposed to solve this problem
and they often led to state-of-the-art performance in terms of average errors.
However, for extreme samples, these methods fail and lead to high errors. In
this paper, we address this limitation by proposing to aggregate different deep
learning methods according to their output uncertainty. We estimate the
relative uncertainty of each approach using Monte Carlo dropout and the final
illumination estimate is obtained as the sum of the different model estimates
weighted by the log-inverse of their corresponding uncertainties. The proposed
framework leads to state-of-the-art performance on INTEL-TAU dataset.Comment: 7 pages,6 figure
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