  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.
