  One of the critical challenges facing imaging studies of the 21-cm signal at
the Epoch of Reionization (EoR) is the separation of astrophysical foreground
contamination. These foregrounds are known to lie in a wedge-shaped region of
$(k_{\perp},k_{\parallel})$ Fourier space. Removing these Fourier modes excises
the foregrounds at grave expense to image fidelity, since the cosmological
information at these modes is also removed by the wedge filter. However, the
21-cm EoR signal is non-Gaussian, meaning that the lost wedge modes are
correlated to the surviving modes by some covariance matrix. We have developed
a machine learning-based method which exploits this information to identify
ionized regions within a wedge-filtered image. Our method reliably identifies
the largest ionized regions and can reconstruct their shape, size, and location
within an image. We further demonstrate that our method remains viable when
instrumental effects are accounted for, using the Hydrogen Epoch of
Reionization Array and the Square Kilometre Array as fiducial instruments. The
ability to recover spatial information from wedge-filtered images unlocks the
potential for imaging studies using current- and next-generation instruments
without relying on detailed models of the astrophysical foregrounds themselves.
