Real-World Super-Resolution via Kernel Estimation and Noise Injection

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.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Video Super-Resolution MSU Super-Resolution for Video Compression RealSR + aomenc BSQ-rate over ERQA 6.762 # 33
BSQ-rate over Subjective Score 0.843 # 15
BSQ-rate over VMAF 4.283 # 59
BSQ-rate over PSNR 15.144 # 73
BSQ-rate over MS-SSIM 5.463 # 62
BSQ-rate over LPIPS 10.915 # 55
Video Super-Resolution MSU Super-Resolution for Video Compression RealSR + uavs3e BSQ-rate over ERQA 1.943 # 16
BSQ-rate over Subjective Score 0.639 # 8
BSQ-rate over VMAF 2.253 # 50
BSQ-rate over PSNR 14.741 # 72
BSQ-rate over MS-SSIM 1.441 # 28
BSQ-rate over LPIPS 1.149 # 8
Video Super-Resolution MSU Super-Resolution for Video Compression RealSR + x265 BSQ-rate over ERQA 1.622 # 11
BSQ-rate over Subjective Score 0.502 # 7
BSQ-rate over VMAF 1.617 # 36
BSQ-rate over PSNR 1.064 # 5
BSQ-rate over MS-SSIM 1.033 # 22
BSQ-rate over LPIPS 1.206 # 9
Video Super-Resolution MSU Super-Resolution for Video Compression RealSR + vvenc BSQ-rate over ERQA 21.965 # 82
BSQ-rate over VMAF 10.67 # 81
BSQ-rate over PSNR 15.144 # 73
BSQ-rate over MS-SSIM 11.643 # 82
BSQ-rate over LPIPS 18.344 # 85
Video Super-Resolution MSU Super-Resolution for Video Compression RealSR + x264 BSQ-rate over ERQA 0.77 # 2
BSQ-rate over Subjective Score 0.196 # 1
BSQ-rate over VMAF 0.775 # 14
BSQ-rate over PSNR 0.675 # 1
BSQ-rate over MS-SSIM 0.487 # 1
BSQ-rate over LPIPS 0.591 # 2
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration RealSR Subjective score 5.286 # 17
ERQAv1.0 0.69 # 12
QRCRv1.0 0 # 21
SSIM 0.767 # 30
PSNR 25.989 # 21
FPS 0.352 # 25
1 - LPIPS 0.911 # 8
Video Super-Resolution MSU Video Upscalers: Quality Enhancement RealSR PSNR 30.64 # 14
LPIPS 0.220 # 15
SSIM 0.900 # 33

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