Amortised MAP Inference for Image Super-resolution

  title={Amortised MAP Inference for Image Super-resolution},
  author={Casper Kaae S\onderby and Jose Caballero and Lucas Theis and Wenzhe Shi and Ferenc Husz{\'a}r},
Image Super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that… CONTINUE READING
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