101 research outputs found
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
cGAN-based Manga Colorization Using a Single Training Image
The Japanese comic format known as Manga is popular all over the world. It is
traditionally produced in black and white, and colorization is time consuming
and costly. Automatic colorization methods generally rely on greyscale values,
which are not present in manga. Furthermore, due to copyright protection,
colorized manga available for training is scarce. We propose a manga
colorization method based on conditional Generative Adversarial Networks
(cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of
training images, our method requires only a single colorized reference image
for training, avoiding the need of a large dataset. Colorizing manga using
cGANs can produce blurry results with artifacts, and the resolution is limited.
We therefore also propose a method of segmentation and color-correction to
mitigate these issues. The final results are sharp, clear, and in high
resolution, and stay true to the character's original color scheme.Comment: 8 pages, 13 figure
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes coarse-resolution feature maps as input, predicts the
high-frequency residuals, and uses transposed convolutions for upsampling to
the finer level. Our method does not require the bicubic interpolation as the
pre-processing step and thus dramatically reduces the computational complexity.
We train the proposed LapSRN with deep supervision using a robust Charbonnier
loss function and achieve high-quality reconstruction. Furthermore, our network
generates multi-scale predictions in one feed-forward pass through the
progressive reconstruction, thereby facilitates resource-aware applications.
Extensive quantitative and qualitative evaluations on benchmark datasets show
that the proposed algorithm performs favorably against the state-of-the-art
methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are
available on http://vllab.ucmerced.edu/wlai24/LapSRN
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