Deep-learning mitigation of foregrounds and beam effects in 21-cm intensity mapping using hybrid frequency differencing and PCA

21-cm intensity mapping (IM) is a powerful technique to probe the large-scale distribution of neutral hydrogen (HI) and extract cosmological information such as the baryon acoustic oscillation feature. A key challenge lies in recovering the faint HI signal from bright foregrounds and frequency-dependent beam effects, which can compromise traditional cleaning methods like principal component analysis (PCA) by removing part of the cosmological signal. Deep-learning approaches have recently been proposed to mitigate these effects by learning mappings between contaminated and true cosmological signals. Building upon our previous work~\citep{2024PhRvD.109f3509S} on the frequency-differencing (FD) method, this study extends the framework to systematically compare FD-based and PCA-based UNet reconstructions using realistic simulations that include foregrounds and beam convolution. We find that both approaches perform comparably without beam or with a Gaussian beam, but under a realistic cosine beam they systematically underestimate the large-scale cross-correlation power spectrum, particularly for $k<0.1 h~\mathrm{Mpc}^{-1}$. To address this limitation, we explore a hybrid approach in which the UNet is trained with two input channels, one constructed from FD and the other from PCA cleaning, allowing the network to simultaneously exploit the strengths of both inputs. This two-channel strategy achieves superior performance, maintaining the cross-correlation power spectrum close to unity on large scales under a cosine beam, improving by 5-8% relative to either FD-based or PCA-based UNet alone. These results demonstrate that providing complementary FD and PCA information to a single deep network is an effective route to robust HI reconstruction, laying the groundwork for precision BAO measurements with future low-redshift 21 cm IM surveys.