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RealSR DF2K
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Our solution is the winner of CVPR NTIRE 2020 Challenge on Real-World Super-Resolution in both tracks.
Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets regardless of blur and noise. However, these methods always fail in real-world image super-resolution, since most of them adopt simple bicubic downsampling from high-quality images to construct Low-Resolution (LR) and High-Resolution (HR) pairs for training which may lose track of frequency-related details. To address this issue, we focus on designing a novel degradation framework for real-world images by estimating various blur kernels as well as real noise distributions. Based on our novel degradation framework, we can acquire LR images sharing a common domain with real-world images. Then, we propose a real-world super-resolution model aiming at better perception. Extensive experiments on synthetic noise data and real-world images demonstrate that our method outperforms the state-of-the-art methods, resulting in lower noise and better visual quality. In addition, our method is the winner of NTIRE 2020 Challenge on both tracks of Real-World Super-Resolution, which significantly outperforms other competitors by large margins.
for corrupted images with processing noise.
Architecture | ESRGAN |
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Scale | 4x |
Size | 64nf23nb |
Color Mode | |
License | Apache-2.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty |
Date | 2020-05-26 |
Dataset | DF2K |