  Low-light image enhancement (LLIE) restores the color and brightness of
underexposed images. Supervised methods suffer from high costs in collecting
low/normal-light image pairs. Unsupervised methods invest substantial effort in
crafting complex loss functions. We address these two challenges through the
proposed TroubleMaker Learning (TML) strategy, which employs normal-light
images as inputs for training. TML is simple: we first dim the input and then
increase its brightness. TML is based on two core components. First, the
troublemaker model (TM) constructs pseudo low-light images from normal images
to relieve the cost of pairwise data. Second, the predicting model (PM)
enhances the brightness of pseudo low-light images. Additionally, we
incorporate an enhancing model (EM) to further improve the visual performance
of PM outputs. Moreover, in LLIE tasks, characterizing global element
correlations is important because more information on the same object can be
captured. CNN cannot achieve this well, and self-attention has high time
complexity. Accordingly, we propose Global Dynamic Convolution (GDC) with O(n)
time complexity, which essentially imitates the partial calculation process of
self-attention to formulate elementwise correlations. Based on the GDC module,
we build the UGDC model. Extensive quantitative and qualitative experiments
demonstrate that UGDC trained with TML can achieve competitive performance
against state-of-the-art approaches on public datasets. The code is available
at https://github.com/Rainbowman0/TML_LLIE.
