Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint
  Image Denoising and Inpainting   This paper studies the challenging problem of fingerprint image denoising and
inpainting. To tackle the challenge of suppressing complicated artifacts (blur,
brightness, contrast, elastic transformation, occlusion, scratch, resolution,
rotation, and so on) while preserving fine textures, we develop a multi-scale
convolutional network, termed U- Finger. Based on the domain expertise, we show
that the usage of dilated convolutions as well as the removal of padding have
important positive impacts on the final restoration performance, in addition to
multi-scale cascaded feature modules. Our model achieves the overall ranking of
No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint
Denoising and Inpainting). Among all participating teams, we obtain the MSE of
0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the
hold-out testing set.

12
The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: stat
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org