Pixel Recursive Super Resolution. Paper @Google Brain. Ryan Dahl, Mohammad Norouzi & Jonathon Shlens


Research ... hoy traemos a este espacio otro paper de Google ... aquí os dejamos el Abstract We present a pixel recursive super resolution model that synthesizes realistic details into images while enhancing their resolution. A low resolution image may correspond to multiple plausible high resolution images, thus modeling the super resolution process with a pixel independent conditional model often results in averaging different details–hence blurry edges. By contrast, our model is able to represent a multimodal conditional distribution by properly modeling the statistical dependencies among the high resolution image pixels, conditioned on a low resolution input. We employ a PixelCNN architecture to define a strong prior over natural images and jointly optimize this prior with a deep conditioning convolutional network. Human evaluations indicate that samples from our proposed model look.(leer

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