论文标题
具有深神经网络的复杂的非sparse QR代码的无光学成像
Optics-free imaging of complex, non-sparse QR-codes with Deep Neural Networks
论文作者
论文摘要
我们使用裸露的图像传感器和经过训练的人工神经网络（ANN）展示了复杂QR代码的光学成像。 ANN经过训练，可以解释原始传感器数据以进行人类可视化。将图像传感器放置在QR码的指定差距上。我们通过实验测试该差距的系统扰动以及QR码对图像传感器的转换和旋转比对来研究ANN的输出，从而研究了方法的鲁棒性。我们的演示为我们打动了使用完全无光学的相机进行复杂的非sparse对象的应用特定成像的可能性。
We demonstrate optics-free imaging of complex QR-codes using a bare image sensor and a trained artificial neural network (ANN). The ANN is trained to interpret the raw sensor data for human visualization. The image sensor is placed at a specified gap from the QR code. We studied the robustness of our approach by experimentally testing the output of the ANNs with system perturbations of this gap, and the translational and rotational alignments of the QR code to the image sensor. Our demonstration opens us the possibility of using completely optics-free cameras for application-specific imaging of complex, non-sparse objects.
