Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Medium Transmission Map Matters for Learning to Restore Real-World
  Underwater Images   Underwater visual perception is essentially important for underwater
exploration, archeology, ecosystem and so on. The low illumination, light
reflections, scattering, absorption and suspended particles inevitably lead to
the critically degraded underwater image quality, which causes great challenges
on recognizing the objects from the underwater images. The existing underwater
enhancement methods that aim to promote the underwater visibility, heavily
suffer from the poor image restoration performance and generalization ability.
To reduce the difficulty of underwater image enhancement, we introduce the
media transmission map as guidance to assist in image enhancement. We formulate
the interaction between the underwater visual images and the transmission map
to obtain better enhancement results. Even with simple and lightweight network
configuration, the proposed method can achieve advanced results of 22.6 dB on
the challenging Test-R90 with an impressive 30 times faster than the existing
models. Comprehensive experimental results have demonstrated the superiority
and potential on underwater perception. Paper's code is offered on:
https://github.com/GroupG-yk/MTUR-Net.

22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an