Transcription of Noise2Noise: Learning Image Restoration without Clean Data
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Noise2Noise: Learning Image Restoration without Clean Data
Noise2 noise : Learning Image Restoration without Clean DataJaakko Lehtinen1 2 Jacob Munkberg1 Jon Hasselgren1 Samuli Laine1 Tero Karras1 Miika Aittala3 Timo Aila1 AbstractWe apply basic statistical reasoning to signal re-construction by machine Learning Learning tomap corrupted observations to Clean signals witha simple and powerful conclusion: it is possi-ble to learn to restore images by only looking atcorrupted examples, at performance at and some-times exceeding training using Clean data, withoutexplicit Image priors or likelihood models of thecorruption. In practice, we show that a singlemodel learns photographic noise removal, denois-ing synthetic Monte Carlo images, and reconstruc-tion of undersampled MRI scans all corruptedby different processes based on noisy data IntroductionSignal reconstruction from corrupted or incomplete mea-surements is an important subfield of statistical data advances in deep neural networks have sparked sig-nificant interest in avoiding the traditional, explicit a prioristatistical modeling of signal corruptions, a
Noise2Noise: Learning Image Restoration without Clean Data 30.5 31 31.5 32 32.5 33 0 20 40 60 80 100 120 140 Additive Gaussian noise ( =25) clean targets noisy targets
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