Super-Resolution Models

SRGAN is a generative adversarial network for single image super-resolution. It uses a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, the authors use a content loss motivated by perceptual similarity instead of similarity in pixel space. The actual networks - depicted in the Figure to the right - consist mainly of residual blocks for feature extraction.

Formally we write the perceptual loss function as a weighted sum of a (VGG) content loss $l^{SR}_{X}$ and an adversarial loss component $l^{SR}_{Gen}$:

$$ l^{SR} = l^{SR}_{X} + 10^{-3}l^{SR}_{Gen} $$

Source: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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