Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionisation. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionised (HII) and neutral (HI) regions during reionisation. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionisation ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionisation progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionisation period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionising source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimise foreground mitigation and enhance 21-cm image construction.