Generation of Plasma Discharge Video from a Single Frame by Using Conditional GAN

At the National Institute for Fusion Science, high-temperature plasma is generated in the Large Helical Device (LHD). Numerous videos capturing plasma discharges during nuclear fusion experiments have been recorded. Analyzing these videos may unveil new discoveries and ensure the safe operation of experiments. Thus, estimating the future states of plasma light emission is essential for optimizing operational parameters in experiments. In current methods for plasma video prediction, a sequence of multiple frames is typically used as input to analyze visual movement trends and predict future plasma discharge conditions or generate future frames in plasma videos. Our study focuses on a more challenging task of video prediction using a single frame of plasma image. We propose a method for predicting sequential future frames containing the image of the visible light from the plasma based on a single given frame by using a conditional GAN model. We experimentally evaluate this approach and find that our proposed method accurately forecasts the future states of plasma images in each frame of the videos.