Optics-free imaging of complex, non-sparse and color QR-codes with deep neural networks

We demonstrate optics-free imaging of complex color and monochrome QR-codes using a bare image sensor and trained artificial neural networks (ANNs). The ANN is trained to interpret the raw sensor data for human visualization. The image sensor is placed at a specified gap (1mm, 5mm and 10mm) 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, non-anthropocentric cameras for application-specific imaging of complex, non-sparse objects.