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arXiv · 1702.00783

Pixel Recursive Super Resolution

Ryan Dahl·Mohammad Norouzi·Jonathon Shlens
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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 more photo realistic than a strong L2 regression baseline.

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Ryan Dahl, Mohammad Norouzi, Jonathon Shlens. 2017-02-02. Pixel Recursive Super Resolution. https://arxiv.org/abs/1702.00783

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