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Real-BlindSR Dataset

Citation Author(s):
GuangYong Chen
WuDing Weng
JianNan Su
Min Gan
C. L. Philip Chen
Submitted by:
Jian-Nan Su
Last updated:
DOI:
10.21227/z2yz-c641
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Abstract

Realistic benchmark datasets are crucial for providing a consistent measurement baseline for comparing different BlindSR methods and testing their generalization ability in real-world scenarios. However, the evaluation of BlindSR methods is still limited by the lack of a common dataset that conforms to realistic degradation scenarios. We construct a benchmark dataset that follows real scenarios, which reflects the real-world BlindSR problem more accurately than existing synthetic datasets. The Real-BlindSR dataset includes diverse degradation types frequently observed in realistic situations, such as different downsampling scales. This dataset offers a comprehensive and realistic benchmark for assessing the performance of BlindSR methods.The Real-BlindSR Dataset we built is mainly used to evaluate the BlindSR task, which includes LR-HR paired images of three different scales of X2, X4, and X8, and includes compression from different Internet platforms and processing of real-world degradation three different tracks.

Instructions:

This dataset offers a comprehensive and realistic benchmark for assessing the performance of BlindSR methods.The Real-BlindSR Dataset we built is mainly used to evaluate the BlindSR task, which includes LR-HR paired images of three different scales of X2, X4, and X8, and includes compression from different Internet platforms and processing of real-world degradation three different tracks.

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