Deep learning for real-time single-pixel video.
Deep learning for real-time single-pixel video.
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DOI：
10.1038/s41598-018-20521-y
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发表时间：
2018-02-05
影响因子：
4.6
通讯作者：
Edgar MP
中科院分区：
文献类型：
--
作者：
Higham CF;Murray-Smith R;Padgett MJ;Edgar MP
单像素相机无需多像素传感器即可捕获图像，从而能够使用最先进的检测器技术，并为可见光谱以外的传感提供潜在的低成本解决方案。单像素相机的一个限制是图像分辨率和帧速率之间的固有权衡，当前的压缩（压缩）感测技术无法支持实时视频。在这项工作中，我们演示了深度学习与卷积自动编码器网络的应用，以2%的压缩比从单像素摄像机采样中恢复30帧/秒的实时128 × 128像素视频。此外，通过在大型图像数据库上训练网络，我们能够优化卷积网络的第一层，相当于优化用于扫描图像强度的基础。这项工作开发并实现了一种新的方法来有效地解决单像素相机的逆问题，并代表了计算成像器的实时操作的重要一步。通过在特定背景下从示例中学习，我们的方法为特定任务的适应提供了高分辨率的可能性，对于气体传感，3D成像和计量应用具有重要意义。
Single-pixel cameras capture images without the requirement for a multi-pixel sensor, enabling the use of state-of-the-art detector technologies and providing a potentially low-cost solution for sensing beyond the visible spectrum. One limitation of single-pixel cameras is the inherent trade-off between image resolution and frame rate, with current compressive (compressed) sensing techniques being unable to support real-time video. In this work we demonstrate the application of deep learning with convolutional auto-encoder networks to recover real-time 128 × 128 pixel video at 30 frames-per-second from a single-pixel camera sampling at a compression ratio of 2%. In addition, by training the network on a large database of images we are able to optimise the first layer of the convolutional network, equivalent to optimising the basis used for scanning the image intensities. This work develops and implements a novel approach to solving the inverse problem for single-pixel cameras efficiently and represents a significant step towards real-time operation of computational imagers. By learning from examples in a particular context, our approach opens up the possibility of high resolution for task-specific adaptation, with importance for applications in gas sensing, 3D imaging and metrology.
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