Low-light enhancement is an important post-image-processing technique, as it helps to reveal hidden details from dark image regions. In this paper, we propose a fast low-light enhancement model, which is robust to various lighting conditions and imaging noise, and is computationally efficient. By using a fusion-based simplified Retinex model, our model caters to different lighting conditions. In the model, we propose an edge-preserving filter to efficiently refine the estimated illumination map. We also extend our model by equipping it with a very simple denoising step, which effectively prevents the over-boosting of imaging noise in the dark regions. We conduct the experiments on public available images as well as the ones collected by ourselves. Visual and quantitative results validate the effectiveness of our model.

弱光增强是一项重要的后期图像处理技术，因为它有助于揭示深色图像区域中的隐藏细节。在本文中，我们提出了一种快速的弱光增强模型，该模型对各种照明条件和成像噪声均具有鲁棒性，并且计算效率高。通过使用基于融合的简化Retinex模型，我们的模型可以满足不同的照明条件。在模型中，我们提出了一个边缘保留滤波器来有效地优化估计的照明图。我们还通过为模型配备非常简单的降噪步骤来扩展模型，从而有效防止了暗区成像噪声的过度增强。我们对公开图像以及我们自己收集的图像进行实验。视觉和定量结果验证了我们模型的有效性。