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:现有的增强极暗环境下拍摄的图像的方法假定最佳输出图像的强度级别已知并已包括在训练集中。然而,这种假设经常不成立,导致输出图像包含视觉瑕疵,例如黑暗区域或低对比度。为了促进自适应模型的训练和评估,可以克服这种限制,我们创建了一个包含1500张在室内和室外低光条件下拍摄的原始图像的数据集。基于我们的数据集,我们介绍了一种深度学习模型,能够在运行时增强输入图像的广泛强度级别,包括在训练期间没有看到的级别。我们的实验结果表明,我们提出的数据集和模型组合可以在各种多样化和具有挑战性的场景中一致和有效地增强图像。