  The near-infrared background between 0.5 $\mu$m to 2 $\mu$m contains a wealth
of information related to radiative processes in the universe. Infrared
background anisotropies encode the redshift-weighted total emission over cosmic
history, including any spatially diffuse and extended contributions. The
anisotropy power spectrum is dominated by undetected galaxies at small angular
scales and diffuse background of Galactic emission at large angular scales. In
addition to these known sources, the infrared background also arises from
intra-halo light (IHL) at $z < 3$ associated with tidally-stripped stars during
galaxy mergers. Moreover, it contains information on the very first galaxies
from the epoch of reionization (EoR). The EoR signal has a spectral energy
distribution (SED) that goes to zero near optical wavelengths due to Lyman
absorption, while other signals have spectra that vary smoothly with frequency.
Due to differences in SEDs and spatial clustering, these components may be
separated in a multi-wavelength-fluctuation experiment. To study the extent to
which EoR fluctuations can be separated in the presence of IHL, extra-galactic
and Galactic foregrounds, we develop a maximum likelihood technique that
incorporates a full covariance matrix among all the frequencies at different
angular scales. We apply this technique to simulated deep imaging data over a
2$\times$100 deg$^2$ sky area from 0.75 $\mu$m to 5 $\mu$m in 9 bands and find
that such a "frequency tomography" can successfully reconstruct both the
amplitude and spectral shape for representative EoR, IHL and the foreground
signals.
