论文标题
学会点燃星光
Learning to Kindle the Starlight
论文作者
论文摘要
由于光污染，专业硬件的要求以及所需的高水平的摄影技能，捕获高度欣赏的恒星现场图像非常具有挑战性。基于深度学习的技术在弱光图像增强（LLIE）方面取得了显着的效果，但由于缺乏训练数据，并未广泛应用于恒星场图像增强。为了解决这个问题，我们构建了第一个恒星场图像增强基准（SFIEB），其中包含355个真实镜头和854个半合成星形磁场图像，所有这些图像都具有相应的参考图像。使用呈现的数据集，我们提出了基于条件降级扩散概率模型（DDPM）的第一个恒星场图像增强方法，即明星。我们将动态随机损坏引入条件DDPM的输入，以提高网络在小规模数据集中的性能和概括。实验显示了我们方法的有希望的结果，这表现优于最先进的低光图像增强算法。数据集和代码将是开源的。
Capturing highly appreciated star field images is extremely challenging due to light pollution, the requirements of specialized hardware, and the high level of photographic skills needed. Deep learning-based techniques have achieved remarkable results in low-light image enhancement (LLIE) but have not been widely applied to star field image enhancement due to the lack of training data. To address this problem, we construct the first Star Field Image Enhancement Benchmark (SFIEB) that contains 355 real-shot and 854 semi-synthetic star field images, all having the corresponding reference images. Using the presented dataset, we propose the first star field image enhancement approach, namely StarDiffusion, based on conditional denoising diffusion probabilistic models (DDPM). We introduce dynamic stochastic corruptions to the inputs of conditional DDPM to improve the performance and generalization of the network on our small-scale dataset. Experiments show promising results of our method, which outperforms state-of-the-art low-light image enhancement algorithms. The dataset and codes will be open-sourced.
