Abstract: We apply basic statistical reasoning to signal reconstruction by machine learning–learning to map corrupted observations to clean signals–with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data. These results have significant implications for ease of training high-performance image restoration models and certain inverse problem solvers.
Joint work with Jacob Munkberg, Jon Hasselgren, Timo Aila, Tero Karras, Samuli Laine, and Miika Aittala.
Speaker: Jaakko Lehtinen
Affiliation: Professor of Computer Science, Aalto University
Place of Seminar: Seminar Room T5, Konemiehentie 2, Aalto University