SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Authors: Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, Lei Zhang

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