The aurora borealis/australis is generated by the interaction between the solar wind and the Earth’s geomagnetic field. Auroral observations provide clues on the mechanisms that transfer energy from the solar wind into the terrestrial environment. However, these auroral displays, which are captured by ground-based optical instruments, are usually in the polar or arctic regions. Weather can obscure the optical instruments from capturing the auroral displays, which limits the number of days good optical data can be recorded. Recent advances in artificial intelligence have provided frameworks that can eliminate clouds on satellite images, for example. This study builds on these developments to address the problem of cloud removal in ground-based camera recordings with the goal of preserving the integrity of the auroral data. We train a deep learning model based on the U-net architecture to learn to reconstruct the aurora borealis from ground-based videos. To manufacture training data, we generate synthetic clouds using Perlin noise. Then, we combine an image with a clear sky with these synthetic clouds to generate a “cloudy” image. The synthetic clouds interlaced with an image frame of a clear sky appear similar to days with real clouds. The model is trained to remove the synthetic clouds. When given real cloudy data, the model effectively extracts auroral features from cloudy days: the filtered image on a real day features fewer clouds with a clearer and more prominent auroral structure when compared to the original image.