  Generative Adversarial Networks (GANs) have been widely used to recover vivid
textures in image super-resolution (SR) tasks. In particular, one discriminator
is utilized to enable the SR network to learn the distribution of real-world
high-quality images in an adversarial training manner. However, the
distribution learning is overly coarse-grained, which is susceptible to virtual
textures and causes counter-intuitive generation results. To mitigate this, we
propose the simple and effective Semantic-aware Discriminator (denoted as SeD),
which encourages the SR network to learn the fine-grained distributions by
introducing the semantics of images as a condition. Concretely, we aim to
excavate the semantics of images from a well-trained semantic extractor. Under
different semantics, the discriminator is able to distinguish the real-fake
images individually and adaptively, which guides the SR network to learn the
more fine-grained semantic-aware textures. To obtain accurate and abundant
semantics, we take full advantage of recently popular pretrained vision models
(PVMs) with extensive datasets, and then incorporate its semantic features into
the discriminator through a well-designed spatial cross-attention module. In
this way, our proposed semantic-aware discriminator empowered the SR network to
produce more photo-realistic and pleasing images. Extensive experiments on two
typical tasks, i.e., SR and Real SR have demonstrated the effectiveness of our
proposed methods.
