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  4. Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
 
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conference paper

Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Rad, Mohammad Saeed  
•
Yu, Thomas  
•
Musat, Claudiu  
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2021
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021
Winter Conference on Applications of Computer Vision (WACV)

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image degradations and analytically modeling these realistic degradations can prove quite difficult. In this work, we propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps. First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs. Second, we take a generic SR network trained on bicubically downsampled images to super-resolve the transformed LR image. The first step of the pipeline addresses the problem by registering the large variety of degraded images to a common, well understood space of images. The second step then leverages the already impressive performance of SR on bicubically downsampled images, sidestepping the issues of end-to-end training on datasets with many different image degradations. We demonstrate the effectiveness of our proposed method by comparing it to recent methods in real-world SR and show that our proposed approach outperforms the state-of-the-art works in terms of both qualitative and quantitative results, as well as results of an extensive user study conducted on several real image datasets.

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Manuscript_RBSR_2021.pdf

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Postprint

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openaccess

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n/a

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9.74 MB

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Adobe PDF

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