Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric Total Variation for Image Vectorization, Zooming and Pixel Art
  Depixelizing   We propose an original method for vectorizing an image or zooming it at an
arbitrary scale. The core of our method relies on the resolution of a geometric
variational model and therefore offers theoretic guarantees. More precisely, it
associates a total variation energy to every valid triangulation of the image
pixels. Its minimization induces a trian-gulation that reflects image
gradients. We then exploit this triangulation to precisely locate
discontinuities, which can then simply be vectorized or zoomed. This new
approach works on arbitrary images without any learning phase. It is
particularly appealing for processing images with low quantization like pixel
art and can be used for depixelizing such images. The method can be evaluated
with an online demonstrator, where users can reproduce results presented here
or upload their own images.

21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
1
The categories of this paper are: cs
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
14
The categories of this paper are: math-ph