论文标题
具有连贯的非线性光学元件的单像素模式识别
Single-Pixel Pattern Recognition with Coherent Nonlinear Optics
论文作者
论文摘要
我们提出并在实验上展示了一种非线性播放方法，可以通过单像素成像和深度神经网络进行模式识别。它采用模式选择性图像上转换，将原始图像投影到一组相干空间模式上，从而可以非线性提取签名特征。使用40个投影模式，MNIST手写数字图像的分类精度达到99.49％，即使将它们与强噪声混合在一起，也达到了95.32％。我们的实验利用非线性光学的丰富相干过程来有效地进行机器学习，并在大型图像的在线分类中进行了潜在的应用，快速发光盆数据分析，复杂的模式识别等。
We propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and deep neural network. It employs mode selective image up-conversion to project a raw image onto a set of coherent spatial modes, whereby its signature features are extracted nonlinear-optically. With 40 projection modes, the classification accuracy reaches a high value of 99.49% for the MNIST handwritten digit images, and up to 95.32% even when they are mixed with strong noise. Our experiment harnesses rich coherent processes in nonlinear optics for efficient machine learning, with potential applications in online classification of large size images, fast lidar data analyses, complex pattern recognition, and so on.
