636 research outputs found
Estimation of Scribble Placement for Painting Colorization
Image colorization has been a topic of interest since
the mid 70’s and several algorithms have been proposed that
given a grayscale image and color scribbles (hints) produce a colorized image. Recently, this approach has been introduced in the field of art conservation and cultural heritage, where B&W photographs of paintings at previous stages have been colorized. However, the questions of what is the minimum number of scribbles necessary and where they should be placed in an image remain unexplored. Here we address this limitation using an iterative algorithm that provides insights as to the relationship between locally vs. globally important scribbles. Given a color image we randomly select scribbles and we attempt to color the
grayscale version of the original.We define a scribble contribution measure based on the reconstruction error. We demonstrate our approach using a widely used colorization algorithm and images from a Picasso painting and the peppers test image. We show that areas isolated by thick brushstrokes or areas with high textural variation are locally important but contribute very little to the
overall representation accuracy. We also find that for the case of Picasso on average 10% of scribble coverage is enough and that flat areas can be presented by few scribbles. The proposed method can be used verbatim to test any colorization algorithm
Probabilistic Image Colorization
We develop a probabilistic technique for colorizing grayscale natural images.
In light of the intrinsic uncertainty of this task, the proposed probabilistic
framework has numerous desirable properties. In particular, our model is able
to produce multiple plausible and vivid colorizations for a given grayscale
image and is one of the first colorization models to provide a proper
stochastic sampling scheme. Moreover, our training procedure is supported by a
rigorous theoretical framework that does not require any ad hoc heuristics and
allows for efficient modeling and learning of the joint pixel color
distribution. We demonstrate strong quantitative and qualitative experimental
results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset
Unsupervised Diverse Colorization via Generative Adversarial Networks
Colorization of grayscale images has been a hot topic in computer vision.
Previous research mainly focuses on producing a colored image to match the
original one. However, since many colors share the same gray value, an input
grayscale image could be diversely colored while maintaining its reality. In
this paper, we design a novel solution for unsupervised diverse colorization.
Specifically, we leverage conditional generative adversarial networks to model
the distribution of real-world item colors, in which we develop a fully
convolutional generator with multi-layer noise to enhance diversity, with
multi-layer condition concatenation to maintain reality, and with stride 1 to
keep spatial information. With such a novel network architecture, the model
yields highly competitive performance on the open LSUN bedroom dataset. The
Turing test of 80 humans further indicates our generated color schemes are
highly convincible
- …