  Despite the breakthroughs in accuracy and speed of single image
super-resolution using faster and deeper convolutional neural networks, one
central problem remains largely unsolved: how do we recover the finer texture
details when we super-resolve at large upscaling factors? The behavior of
optimization-based super-resolution methods is principally driven by the choice
of the objective function. Recent work has largely focused on minimizing the
mean squared reconstruction error. The resulting estimates have high peak
signal-to-noise ratios, but they are often lacking high-frequency details and
are perceptually unsatisfying in the sense that they fail to match the fidelity
expected at the higher resolution. 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. To achieve this, we propose a
perceptual loss function which consists of an adversarial loss and a content
loss. The adversarial loss pushes our 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, we use
a content loss motivated by perceptual similarity instead of similarity in
pixel space. Our deep residual network is able to recover photo-realistic
textures from heavily downsampled images on public benchmarks. An extensive
mean-opinion-score (MOS) test shows hugely significant gains in perceptual
quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of
the original high-resolution images than to those obtained with any
state-of-the-art method.
