  We applied Deep Learning algorithm known as Generative Adversarial Networks
(GANs) to perform solar image-to-image translation. That is, from Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager(HMI) line of sight
magnetogram images to SDO/Atmospheric Imaging Assembly(AIA) 0304-{\AA} images.
The Ultraviolet(UV)/Extreme Ultraviolet(EUV) observations like the
SDO/AIA0304-{\AA} images were only made available to scientists in the late
1990s even though the magenetic field observations like the SDO/HMI have been
available since the 1970s. Therefore by leveraging Deep Learning algorithms
like GANs we can give scientists access to complete datasets for analysis. For
generating high resolution solar images we use the Pix2PixHD and Pix2Pix
algorithms. The Pix2PixHD algorithm was specifically designed for high
resolution image generation tasks, and the Pix2Pix algorithm is by far the most
widely used image to image translation algorithm. For training and testing we
used the data for the year 2012, 2013 and 2014. The results show that our deep
learning models are capable of generating high resolution(1024 x 1024 pixels)
AIA0304 images from HMI magnetograms. Specifically, the pixel-to-pixel Pearson
Correlation Coefficient of the images generated by Pix2PixHD and original
images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate
images. The results we get for our Pix2PixHD model is better than the results
obtained by previous works done by others to generate AIA0304 images. Thus, we
can use these models to generate AIA0304 images when the AIA0304 data is not
available which can be used for understanding space weather and giving
researchers the capability to predict solar events such as Solar Flares and
Coronal Mass Ejections. As far as we know, our work is the first attempt to
leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image
translation.
