论文标题
极端低光图像的自适应增强
Adaptive Enhancement of Extreme Low-Light Images
论文作者
论文摘要
在非常低光的环境中捕获的黑暗图像的现有方法假设最佳输出图像的强度水平已知并已经包含在训练集中。但是，这个假设通常无法成立，导致输出图像包含视觉缺陷，例如黑暗区域或低对比度。为了促进可以克服这一限制的自适应模型的培训和评估，我们创建了一个在室内和室外低光条件下拍摄的1500张原始图像的数据集。基于我们的数据集，我们引入了一个深度学习模型，能够增强运行时强度范围的输入图像，包括在训练过程中没有看到的图像。我们的实验结果表明，我们提出的数据集与我们的模型相结合可以一致有效地增强各种不同和具有挑战性的情况的图像。
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.
