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tokens: this case, the inpainting performances with DP and GAN can be affected by noise, and the reconstruction area can be visually distinguished in the square patch. Figure 12. Thumbnail images of Planck temperature maps at (a) 353 and (b) 857 GHz. The radius of the reconstructed area (black circle) is 15′. Two locations are chosen to highlight different inpainting performances with high and low SNRs of Planck 353 map, respectively, at high Galactic latitude, i.e., and at low Galactic latitude, i.e., . For a more quantitative assessment, we thus estimate the summary statistics shown in Section 4.1 and we show the results in Figure 13 . Figure 13. Summary statistics estimated on Planck dust T maps at (a) 353 and (b) 857 GHz. A clear indication that the thermal dust emission data is contaminated by instrumental noise can be inferred by comparing the shapes of Minkowski functionals estimated for the maps at 353 GHz, with the ones from signal-only simulations (Figure 4 ) or from signal-dominated maps (Figure 13 (b)). The morphology of the Minkowski functionals of the former largely resembles the functionals estimated from a Gaussian signal. Two possible candidates for the Gaussian component are (i) Planck instrumental noise, which can be approximated as white within patches, and (ii) CMB residual emission. DP inpaintings are the most affected ones in the presence of the noise at , e.g., notice how the pixel distribution (top left panel) of Figure 13 (a) noticeably departs from the ground-truth one. However, the KS test p -value performed on the DP and ground-truth samples is , i.e., not significantly low enough to reject the null hypothesis that the two samples are different. On the other hand, inpainting with GAN and NN does not show any dependence with SNR, as the performances with these methodologies are essentially similar to the ones observed with the signal-only simulated maps (e.g., Figures 7 , 8 , and 10 ). Finally, we would like to point out that for the case of inpainting with GAN, we use the weights derived from the training set composed of signal-only dust TQU simulated maps. Looking at Figure 13 we notice that GAN is able to statistically reproduce at 353 GHz the features composed by signal and noise, indicating that the network has correctly learned the features related to the intrinsic signal in the presence of noise, and injects signal plus noise features statistically coherent with the ones outside the masked area. However, when the noise is highly dominating in the patch, we can clearly distinguish smooth artifacts in some cases inpainted with GAN (see Figure 12 (a, bottom)). This is somewhat expected because GAN is trained on signal-only simulated images. Further investigations on training GAN with noisy data are needed, and we will address it in a future work. 4.3. Inpainting Maps with Point Sources In this section, we aim at showing real world applications of the three reconstruction methodologies by inpainting areas in real maps