  The radioheliograph image is essential for the study of solar short term
activities and long term variations, while the continuity and granularity of
radioheliograph data is not so ideal, due to the short visible time of the sun
and the complex electron-magnetic environment near the ground-based radio
telescope. In this work, we develop a multi-channel input single-channel output
neural network, which can generate radioheliograph image in microwave band from
the Extreme Ultra-violet (EUV) observation of the Atmospheric Imaging Assembly
(AIA) on-board the Solar Dynamic Observatory (SDO). The neural network is
trained with nearly 8 years of data of Nobeyama Radioheliograph (NoRH) at 17
GHz and SDO/AIA from January 2011 to September 2018. The generated
radioheliograph image is in good consistency with the well-calibrated NoRH
observation. SDO/AIA provides solar atmosphere images in multiple EUV
wavelengths every 12 seconds from space, so the present model can fill the
vacancy of limited observation time of microwave radioheliograph, and support
further study of the relationship between the microwave and EUV emission.
