121,855 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
Image-Processing Techniques for the Creation of Presentation-Quality Astronomical Images
The quality of modern astronomical data, the power of modern computers and
the agility of current image-processing software enable the creation of
high-quality images in a purely digital form. The combination of these
technological advancements has created a new ability to make color astronomical
images. And in many ways it has led to a new philosophy towards how to create
them. A practical guide is presented on how to generate astronomical images
from research data with powerful image-processing programs. These programs use
a layering metaphor that allows for an unlimited number of astronomical
datasets to be combined in any desired color scheme, creating an immense
parameter space to be explored using an iterative approach. Several examples of
image creation are presented.
A philosophy is also presented on how to use color and composition to create
images that simultaneously highlight scientific detail and are aesthetically
appealing. This philosophy is necessary because most datasets do not correspond
to the wavelength range of sensitivity of the human eye. The use of visual
grammar, defined as the elements which affect the interpretation of an image,
can maximize the richness and detail in an image while maintaining scientific
accuracy. By properly using visual grammar, one can imply qualities that a
two-dimensional image intrinsically cannot show, such as depth, motion and
energy. In addition, composition can be used to engage viewers and keep them
interested for a longer period of time. The use of these techniques can result
in a striking image that will effectively convey the science within the image,
to scientists and to the public.Comment: 104 pages, 38 figures, submitted to A
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