  The cosmic microwave background (CMB) stands as a pivotal source for studying
weak gravitational lensing. While the lensed CMB aids in constraining
cosmological parameters, it simultaneously smooths the original CMB's features.
The angular power spectrum of the unlensed CMB showcases sharper acoustic peaks
and more pronounced damping tails, enhancing the precision of inferring
cosmological parameters that influence these aspects. Although delensing
diminishes the $B$-mode power spectrum, it facilitates the pursuit of
primordial gravitational waves and enables a lower variance reconstruction of
lensing and additional sources of secondary CMB anisotropies. In this work, we
explore the potential of deep learning techniques, specifically the U-Net++
algorithm, to play a pivotal role in CMB delensing. We analyze three fields,
namely $T$, $Q$, and $U$ sky maps, present the angular power spectra of the CMB
delensed $TT$, $EE$, $BB$, and $TE$, and compare them with the unlensed CMB
angular power spectra. Our findings reveal that the angular power spectrum of
the lensed CMB, processed by U-Net++, closely aligns with that of the unlensed
CMB. Thus, U-Net++ based CMB delensing proves to be effective in mitigating the
impacts of weak gravitational lensing, paving the way for enhancing the CMB
delensing power spectrum in forthcoming CMB experiments. The code utilized for
this analysis is available on GitHub.
