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.

翻译：低光照图像增强（LLIE）旨在恢复曝光不足图像的颜色与亮度。监督方法需要收集低光照/正常光照图像对，成本高昂；无监督方法则需投入大量精力设计复杂损失函数。我们提出的麻烦制造者学习（TML）策略通过采用正常光照图像作为训练输入，同时解决了这两个挑战。TML的核心思想简单直接：先对输入图像进行调暗处理，再提升其亮度。该策略基于两个关键组件：首先，麻烦制造者模型（TM）从正常图像中构建伪低光照图像，以减轻成对数据的采集成本；其次，预测模型（PM）增强伪低光照图像的亮度。此外，我们引入增强模型（EM）进一步改善PM输出的视觉效果。在LLIE任务中，表征全局元素相关性至关重要，因为同物体的更多信息可被捕获。卷积神经网络难以有效实现这一目标，而自注意力机制存在时间复杂度高的缺陷。为此，我们提出时间复杂度为O(n)的全局动态卷积（GDC），该模块通过模仿自注意力机制的部分计算过程来构建元素间相关性。基于GDC模块，我们构建了UGDC模型。大量定量与定性实验表明，采用TML训练的UGDC在公共数据集上能够取得与现有最先进方法相媲美的性能。代码开源地址：https://github.com/Rainbowman0/TML_LLIE。