论文标题
使用深神经网络的无光学图像分类
Classification of optics-free images with deep neural networks
论文作者
论文摘要
通过删除所有光学元件，仅留下图像传感器来实现最薄的相机。我们在无需拟人化图像重建的情况下，在无光学图像上训练深层神经网络，在无光学图像上执行多类检测和二进制分类（准确性为92％）。从无光学图像中推断有可能提高隐私和功率效率。
The thinnest possible camera is achieved by removing all optics, leaving only the image sensor. We train deep neural networks to perform multi-class detection and binary classification (with accuracy of 92%) on optics-free images without the need for anthropocentric image reconstructions. Inferencing from optics-free images has the potential for enhanced privacy and power efficiency.
