In this work, we introduce a dual transformation network for single image contrast enhancement, which usually aims to improve global contrast and enrich local details. To this end, we propose two parallel branches to respectively handle the two goals by learning different kinds of transformations. Specifically, one branch aims to construct a global transformation curve to improve global contrast, while the other one directly predicts pixel offsets to enrich local details. In addition, we further design a differentiable histogram loss to provide supervised information related to the global contrast. In this way, the network training can be guided by different constraints, e.g., pixel-level mean squared error and statistics-level histogram error. Experiments demonstrate that our method can be effectively applied to various contrast conditions with favorable performance against the state-of-the-art methods.

中文翻译：

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学习图像对比度增强的双变换网络

在这项工作中，我们引入了用于单幅图像对比度增强的双变换网络，其通常旨在提高全局对比度并丰富局部细节。为此，我们提出了两个并行分支，通过学习不同类型的转换来分别处理两个目标。具体来说，一个分支旨在构建全局变换曲线以提高全局对比度，而另一个分支直接预测像素偏移以丰富局部细节。此外，我们进一步设计了可微直方图损失以提供与全局对比度相关的监督信息。这样，网络训练可以通过不同的约束来指导，例如像素级均方误差和统计级直方图误差。