Natural scenes commonly
present a
wide dynamic range, and the human visual system is able to capture
subtle details in both dark and bright areas. This is not the case for
standard digital cameras,
which are limited in the dynamic range they are able to represent. In
particular, standard cameras are only able to capture different
intervals of the luminance range at different exposure times,
in
particular, bright areas are captured at short exposure times,
while dark areas are captured at longer exposure times. High
dynamic range
imaging aims at fusing different camera outputs to obtain a
new
image
with information in both the dark and bright areas of the
scene.
Related Publications:
[GVB
2015] - The
Intrinsic Error of Exposure Fusion for
HDR Imaging, and a Way to Reduce it
Images
obtained under adverse weather conditions, such as haze or fog,
typically exhibit low contrast and faded colors, which may severely
limit the visibility within the scene. Unveiling the image structure
under the haze layer and recovering vivid colors out of a single image
remains a challenging task, since the degradation is depth-dependent
and conventional methods are unable to overcome this problem.
Related Publications:
[GVP
2016] - Fusion-based variational image dehazing
[GVP
2015] - Enhanced variational image dehazing
Blind gamma estimation
is the problem of estimating the gamma function
that is applied to a linear image both for perceptual reasons and for
the compensation of the non-linear behaviour of displays. Gamma values
change both inter- and
intra-camera. In the latter case, the change comes from the use of
different scene settings.
Related Publications:
[VaB
2014] - Simultaneous blind gamma estimation
We expect two pictures
of the same scene, taken under the same
illumination, to be consistent in terms of color. But if we have used
different cameras to take the pictures, or just a single camera with
automatic white balance (AWB) and/or automatic exposure (AE)
correction, then the most common situation is that there are objects in
the scene for which the color appearance is different in the two shots.
This problem is even aggravated when we are using different cameras.
The goal of
color stabilization is to convert one of the images to look exactly as
the other one in terms of color.
Related Publications:
[VaB
2014] - Color stabilization across time and along
shots of
the same scene for one or several cameras of unknown specifications
Color camera
characterization, mapping outputs from the camera sensors
to an independent color space such as XYZ, is an important step in the
camera processing pipeline. We proposed a method that aims at
minimizing the perceptual error of the characterization.
Related Publications:
[VCB
2014] - Perceptual color characterization of cameras
Gamut mapping transforms
the
colors of an input image to the colors of a target device to exploit
the full color potential of the rendering device. This problem is
highly relevant in industry as new displays with large gamut
capabilities are reaching the market. We have proposed different
solutions that are based on applying iterative schemes to a
perceptually-inspired variational method .
Related Publications:
[ZVB 2017]- Gamut extension
for cinema
[ZVB
2014] - Gamut mapping in cinematography through
Perceptual-based contrast modification
Noise
is present in images due to the inherent physical and technological
limitations of the cameras. The presence of noise degrades the quality
of the captured images. Therefore, image denoising is a must-have step
in the digital imaging processing pipeline. However, very few attention
has been paid in how to use color information for this goal. We have
proposed a color decomposition framework to pre-process an
image
before applying a typical denoising method.
Related Publications:
[VaB
2018] - Angular-based preprocessing for image
denoising
Colour constancy is the
ability of the human
visual system to perceive a stable representation of colour despite
illumination changes. Computational colour constancy tries to emulate
this ability, that is, tries to recover the illuminant of a scene from
an acquired image.
Related Publications:
[VVB
2012] - Color Constancy by Category Correlation
[VPV 2009] - Color Constancy Algorithms:
Psychophysical Evaluation on a New Dataset
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