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Research Article | Open Access

Exploring contextual priors for real-world image super-resolution

Shixiang Wu1,2, Chao Dong1,3, Yu Qiao1,3( )
Guangdong–Hong Kong–Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
University of Chinese Academy of Sciences, Beijing 100049, China
Shanghai AI Laboratory, Shanghai, China
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Abstract

Real-world blind image super-resolution is a challenging problem due to the absence of target high resolution images for training. Inspired by the recent success of the single image generation based method SinGAN, we tackle this challenging problem with a refined model SR-SinGAN, which can learn to perform single real image super-resolution. Firstly, we empirically find that downsampled LR input with an appropriate size can improve the robustness of the generation model. Secondly, we introduce a global contextual prior to provide semantic information. This helps to remove distorted pixels and improve the output fidelity. Finally, we design an image gradient based local contextual prior to guide detail generation. It can alleviate generated artifacts in smooth areas while preserving rich details in densely textured regions (e.g., hair, grass). To evaluate the effectiveness of these contextual priors, we conducted extensive experiments on both artificial and real images. Results show that these priors can stabilize training and preserve output fidelity, improving the generated image quality. We furthermore find that these single image generation based methods work better for images with repeated textures compared to general images.

Keywords

unsupervised learningblind super-resolutionimage contextimage generation

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Computational Visual Media
Volume 11 Issue 1,
February 2025
Pages 159-177
DOI: 10.26599/CVM.2025.9450303
Cite this article:
Wu S, Dong C, Qiao Y. Exploring contextual priors for real-world image super-resolution. Computational Visual Media, 2025, 11(1): 159-177. https://doi.org/10.26599/CVM.2025.9450303

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Received: 06 April 2022
Accepted: 26 June 2022
Published: 28 February 2025
© The Author(s) 2025.

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