Fig. 2

Experimental results of different channels to transmit information under coherent and incoherent illumination. Two digits “5” and “8” are used as the ground truths. For each case, the recorded patterns and the corresponding reconstructed images are shown side by side. (Upper panels) Under coherent illumination, the recorded patterns after the aperture are the diffraction patterns of the input objects. The trained network can reconstruct the corresponding images. After inserting the diffuser, the camera records high contrast speckle patterns, and the aperture channel is encoded by the randomly distributed refractive index on the diffuser. Information can still be delivered to the detection plane and extracted by deep learning. (Lower panels) Under incoherent illumination, through the aperture, no image can be extracted from the pattern. But with the presence of the diffuser, images can again be well predicted from the speckle patterns, although the speckles are of lower contrast than those formed under coherent illumination. The rain effect on the recorded patterns, i.e., the pattern has an appearance of rain falling on a window, in the aperture case is due to the rotating of the diffuser to generate the pseudo thermal source