Fig. 3

Experimental results of grid and diffuser channels under incoherent illumination. a Recorded PSFs of grids 1&2 at different point positions, which confirm that the grid systems are shift-invariant. The correlation coefficient curves are flat over the scan range, which is already bigger than input objects. b Photograph of the input digit “5”. c Row A: the PSFs of different systems (Grid I, Grid II, and diffuser). From the zoom-ins we can see many fine structures. Row B: the calculated convolution of digit “5” and the corresponding PSFs. Row C: the corresponding recorded patterns by the camera. In the column for the diffuser, after contrast enhancement by the imadjust function in Matlab, fine structures, which cannot be seen in the original recorded pattern 1), become visible in 2). Rows D & E are extracted images from recorded patterns correspondingly by deep learning and deconvolution, respectively. DL is short for deep learning. Rows F & G are reconstructed images from the calculated convolution patterns by deep learning and deconvolution, respectively. The red dash square inserts show the zoom-in of original areas correspondingly. Compared to deconvolution operation, deep learning shows advantages in noise suppressing in reconstructed images