基于多粒度协作网络的极弱光成像
Extreme Low-Light Imaging with Multi-granulation Cooperative Networks
摘要：
由于低信噪比、复杂的图像内容以及极端低光条件下拍摄场景的多样性，低光成像具有挑战性，因为图像可能看起来很暗且有噪声。已经提出了许多方法来提高在极端低光条件下的成像质量，但是仍然难以获得令人满意的结果，特别是当它们试图保持高动态范围（HDR）时。在本文中，我们提出了一种新的方法，多粒度合作网络（MCN）的双向信息流，以增强极端低光图像，并设计了一个照明地图估计函数（IMEF），以保持高动态范围（HDR）。为了促进这项研究，我们还有助于创建一个新的基准数据集的真实世界黑暗高动态范围（DHDR）的图像，以评估在低光环境下的高动态保存的性能。实验结果表明，该方法在视觉效果和定量分析方面都优于现有的方法。
英文摘要：
Low-light imaging is challenging since images may appear to be dark and noised due to low signal-to-noise ratio, complex image content, and the variety in shooting scenes in extreme low-light condition. Many methods have been proposed to enhance the imaging quality under extreme low-light conditions, but it remains difficult to obtain satisfactory results, especially when they attempt to retain high dynamic range (HDR). In this paper, we propose a novel method of multi-granulation cooperative networks (MCN) with bidirectional information flow to enhance extreme low-light images, and design an illumination map estimation function (IMEF) to preserve high dynamic range (HDR). To facilitate this research, we also contribute to create a new benchmark dataset of real-world Dark High Dynamic Range (DHDR) images to evaluate the performance of high dynamic preservation in low light environment. Experimental results show that the proposed method outperforms the state-of-the-art approaches in terms of both visual effects and quantitative analysis.
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