Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Underwater image enhancement with Image Colorfulness Measure   Due to the absorption and scattering effects of the water, underwater images
tend to suffer from many severe problems, such as low contrast, grayed out
colors and blurring content. To improve the visual quality of underwater
images, we proposed a novel enhancement model, which is a trainable end-to-end
neural model. Two parts constitute the overall model. The first one is a
non-parameter layer for the preliminary color correction, then the second part
is consisted of parametric layers for a self-adaptive refinement, namely the
channel-wise linear shift. For better details, contrast and colorfulness, this
enhancement network is jointly optimized by the pixel-level and
characteristiclevel training criteria. Through extensive experiments on natural
underwater scenes, we show that the proposed method can get high quality
enhancement results.

27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph