De-noising SDO/HMI Solar Magnetograms by Image Translation Method Based on Deep Learning

In astronomy, long-exposure observations are one of the important ways to improve signal-to-noise ratios (S/Ns). In this Letter, we apply a deep-learning model to de-noise solar magnetograms. This model is based on a deep convolutional generative adversarial network with a conditional loss for image-to-image translation from a single magnetogram (input) to a stacked magnetogram (target). For the input magnetogram, we use Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line-of-sight magnetograms at the center of the solar disk. For the target magnetogram, we make 21-frame-stacked magnetograms, taking into account solar rotation at the same position. We train a model using 7004 pairs of the input and target magnetograms from 2013 January to 2013 October. We then validate the model using 707 pairs from 2013 November and test the model using 736 pairs from 2013 December. Our results from this study are as follows. First, our model successfully de-noises SDO/HMI magnetograms, and the de-noised magnetograms from our model are mostly consistent with the target magnetograms. Second, the average noise level of the de-noised magnetograms is greatly reduced from 8.66 to 3.21 G, and it is consistent with that of the target magnetograms, 3.21 G. Third, the average pixel-to-pixel correlation coefficient value increases from 0.88 (input) to 0.94 (de-noised), which means that the de-noised magnetograms are more consistent with the target ones than the input ones. Our results can be applied to many scientific fields in which the integration of many frames (or long-exposure observations) are used to improve the S/N.