  This paper addresses a near-field imaging problem utilizing extremely
large-scale multiple-input multiple-output (XL-MIMO) antennas and
reconfigurable intelligent surfaces (RISs) already in place for wireless
communications. To this end, we consider a system with a fixed transmitting
antenna array illuminating a region of interest (ROI) and a fixed receiving
antenna array inferring the ROI's scattering coefficients. Leveraging XL-MIMO
and high frequencies, the ROI is situated in the radiative near-field region of
both antenna arrays, thus enhancing the degrees of freedom (DoF) (i.e., the
channel matrix rank) of the illuminating and sensing channels available for
imaging, here referred to as holographic imaging. To further boost the imaging
performance, we optimize the illuminating waveform by solving a min-max
optimization problem having the upper bound of the mean squared error (MSE) of
the image estimate as the objective function. Additionally, we address the
challenge of non-line-of-sight (NLOS) scenarios by considering the presence of
a RIS and deriving its optimal reflection coefficients. Numerical results
investigate the interplay between illumination optimization, geometric
configuration (monostatic and bistatic), the DoF of the illuminating and
sensing channels, image estimation accuracy, and image complexity.
