Existing methods for enhancing dark images captured in a very low-light environment assume that the intensity level of the optimal output image is known and already included in the training set. However, this assumption often does not hold, leading to output images that contain visual imperfections such as dark regions or low contrast. To facilitate the training and evaluation of adaptive models that can overcome this limitation, we have created a dataset of 1500 raw images taken in both indoor and outdoor low-light conditions. Based on our dataset, we introduce a deep learning model capable of enhancing input images with a wide range of intensity levels at runtime, including ones that are not seen during training. Our experimental results demonstrate that our proposed dataset combined with our model can consistently and effectively enhance images across a wide range of diverse and challenging scenarios.

翻译：现有针对极低光环境下拍摄的暗图像增强方法，均假设最优输出图像的强度水平已知且已包含在训练集中。然而，这一假设往往不成立，导致输出图像出现暗区域或低对比度等视觉缺陷。为促进能够克服此局限性的自适应模型的训练与评估，我们创建了包含1500张原始图像的公开数据集，涵盖室内与室外低光场景。基于该数据集，我们提出一种深度学习模型，能够在运行时增强具有广泛强度水平的输入图像（包括训练中未见的强度级别）。实验结果表明，所提出的数据集与模型组合能够持续有效地增强多种多样且具有挑战性场景下的图像。