Photo-realistic single image super-resolution using a generative adversarial network
Photo-realistic single image super-resolution using a generative adversarial network Ledig et al., arXiv’16
Today’s paper choice also addresses an image-to-image translation problem, but here we’re interested in one specific challenge: super-resolution. In super-resolution we take as input a low resolution image like this:

And produce as output an estimation of a higher-resolution up-scaled version:

For the example above, here’s the ground truth hi-resolution image from which the low-res input was initially generated:

Especially challenging of course, is to recover / generate realistic looking finer texture details when super-resolving at large upscaling factors. (Look at the detail around the hat band and neckline in the above figures for example).
In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors.
In a mean-opinion score test, the scores obtained by SRGAN are closer to those of the original high-resolution images than those obtained by any other state-of-the-art method.
Here’s an example of the fine-detail SRGAN can create, even when upscaling by a factor of 4. Note how close it is to the original.

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