Imaging of a micro-structure: binary contrast source inversion and convolutional neural networks

Time-harmonic transverse-magnetic electromagnetic imaging of a grid-like, finite set of circular cylindrical dielectric rods placed in air with sub-wavelength distances between adjacent rods and sub-wavelength diameters is considered. It is shown how one can be achieving super-resolution in the resulting micro-structure, reaching beyond the Rayleigh criterion, using a binary-specialized form of teh contrast source inversion method and another method based on convolutional neural networks. illustrations of both methods are proposed from comprehensive numerical simulations in a number of configurations of interest, this seen in terms of organization of the micro-structure itself, discrete frequencies of observation, data acquisition and data noise. Experimental validations are proposed as well from experiments carried outt in an anechoic chamber.