20 research outputs found
Probabilistic Color Constancy
In this paper, we propose a novel unsupervised color constancy method, called
Probabilistic Color Constancy (PCC). We define a framework for estimating the
illumination of a scene by weighting the contribution of different image
regions using a graph-based representation of the image. To estimate the weight
of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar
colors contribute similarly and darker (super-)pixels contribute less. The
resulting system has one global optimum solution. The proposed method achieves
competitive performance, compared to the state-of-the-art, on INTEL-TAU
dataset.Comment: 5 pages, 1 figur
Template matching with white balance adjustment under multiple illuminants
In this paper, we propose a novel template matching method with a white
balancing adjustment, called N-white balancing, which was proposed for
multi-illuminant scenes. To reduce the influence of lighting effects, N-white
balancing is applied to images for multi-illumination color constancy, and then
a template matching method is carried out by using adjusted images. In
experiments, the effectiveness of the proposed method is demonstrated to be
effective in object detection tasks under various illumination conditions.Comment: \c{opyright} 2022 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Practical cross-sensor color constancy using a dual-mapping strategy
Deep Neural Networks (DNNs) have been widely used for illumination
estimation, which is time-consuming and requires sensor-specific data
collection. Our proposed method uses a dual-mapping strategy and only requires
a simple white point from a test sensor under a D65 condition. This allows us
to derive a mapping matrix, enabling the reconstructions of image data and
illuminants. In the second mapping phase, we transform the re-constructed image
data into sparse features, which are then optimized with a lightweight
multi-layer perceptron (MLP) model using the re-constructed illuminants as
ground truths. This approach effectively reduces sensor discrepancies and
delivers performance on par with leading cross-sensor methods. It only requires
a small amount of memory (~0.003 MB), and takes ~1 hour training on an
RTX3070Ti GPU. More importantly, the method can be implemented very fast, with
~0.3 ms and ~1 ms on a GPU or CPU respectively, and is not sensitive to the
input image resolution. Therefore, it offers a practical solution to the great
challenges of data recollection that is faced by the industry
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