论文标题
数字世界：端到端的深度图像稳定，可学习的曝光时间
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times
论文作者
论文摘要
使用驱动的gimbals机械图像稳定可以捕获长期暴露镜头，而不会因摄像机运动而遭受模糊。但是，这些设备通常在物理上繁琐且昂贵，从而限制了它们的广泛使用。在这项工作中，我们建议通过快速未稳定相机的输入来数字地模拟机械稳定的系统。为了在长期暴露时利用运动模糊与短曝光下的低SNR之间的权衡，我们训练一个CNN，通过汇总一系列嘈杂的短曝光框架来估计尖锐的高SNR图像，这与未知运动有关。我们进一步建议以端到端的方式学习爆发的暴露时间，从而平衡整个框架的噪音和模糊。我们证明了该方法比传统方法的优势，即在合成数据和真实数据上脱毛或确定固定暴露爆发。
Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-off between motion blur at long exposures and low SNR at short exposures, we train a CNN that estimates a sharp high-SNR image by aggregating a burst of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst's exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method's advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data.
