Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Invertible Network for Unpaired Low-light Image Enhancement   Existing unpaired low-light image enhancement approaches prefer to employ the
two-way GAN framework, in which two CNN generators are deployed for enhancement
and degradation separately. However, such data-driven models ignore the
inherent characteristics of transformation between the low and normal light
images, leading to unstable training and artifacts. Here, we propose to
leverage the invertible network to enhance low-light image in forward process
and degrade the normal-light one inversely with unpaired learning. The
generated and real images are then fed into discriminators for adversarial
learning. In addition to the adversarial loss, we design various loss functions
to ensure the stability of training and preserve more image details.
Particularly, a reversibility loss is introduced to alleviate the over-exposure
problem. Moreover, we present a progressive self-guided enhancement process for
low-light images and achieve favorable performance against the SOTAs.

33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th