A Deep Learning Approach for Reconstruction in Millimeter-Wave Imaging Systems

In millimeter-wave (MMW) imaging, the objects of interest
are oftentimes modeled as 2D binary (black and white) shapes with white
pixels representing the reflecting interior of the object. However, due to
propagation of the scattered waves, the continuous-domain binary images
are convolved with a so-called point-spread function (PSF) before being
digitized by means of sampling. As the 2D PSF is both non-separable
and non-vanishing in the case of MMW imaging, exact recovery is quite
complicated. In this paper, we propose a deep learning approach for
image reconstruction. We should highlight that the wave scatterings
are suitably represented with complex-valued quantities, while standard
deep neural networks (DNN) accept real-valued inputs. To overcome
this challenge, we separate the real and imaginary parts as if we had
two imaging modalities and concatenate them to form a real-valued
input with larger size. Fortunately, the network automatically learns
how to combine the mutual information between these modalities to
reconstruct the final image. Among the advantages of the proposed
method are improved robustness against additive noise and mismatch
errors of imaging frequency and object to antenna distance; indeed, the
method works well in wide-band imaging scenarios over a wide range of
object to antenna distances even in presence of high noise levels without
requiring a separate calibration stage. We test the method with synthetic
data simulated with software as well as real recordings in the laboratory.