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We demonstrate optics-free imaging of complex QR-codes using a bare imagesensor and a trained artificial neural network (ANN). The ANN is trained tointerpret the raw sensor data for human visualization. The image sensor isplaced at a specified gap from the QR code. We studied the robustness of ourapproach by experimentally testing the output of the ANNs with systemperturbations of this gap, and the translational and rotational alignments ofthe QR code to the image sensor. Our demonstration opens us the possibility ofusing completely optics-free cameras for application-specific imaging ofcomplex, non-sparse objects.

Optics-free imaging of complex, non-sparse QR-codes with Deep Neural Networks