  Owe to the powerful generative priors, the pre-trained text-to-image (T2I)
diffusion models have become increasingly popular in solving the real-world
image super-resolution problem. However, as a consequence of the heavy quality
degradation of input low-resolution (LR) images, the destruction of local
structures can lead to ambiguous image semantics. As a result, the content of
reproduced high-resolution image may have semantic errors, deteriorating the
super-resolution performance. To address this issue, we present a
semantics-aware approach to better preserve the semantic fidelity of generative
real-world image super-resolution. First, we train a degradation-aware prompt
extractor, which can generate accurate soft and hard semantic prompts even
under strong degradation. The hard semantic prompts refer to the image tags,
aiming to enhance the local perception ability of the T2I model, while the soft
semantic prompts compensate for the hard ones to provide additional
representation information. These semantic prompts encourage the T2I model to
generate detailed and semantically accurate results. Furthermore, during the
inference process, we integrate the LR images into the initial sampling noise
to mitigate the diffusion model's tendency to generate excessive random
details. The experiments show that our method can reproduce more realistic
image details and hold better the semantics. The source code of our method can
be found at https://github.com/cswry/SeeSR.
