Retrospective artifact removal using supervised learning requires explicit generation of artifact-corrupted images and is impractical since generating the wide variety of potential artifacts can be challenging. Using unsupervised learning, we show how artifacts can be disentangled with remarkable efficacy from artifact-corrupted images to recover the artifact-free counterparts, without requiring explicit artifact generation.
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