Real Blur Dataset
Introduced by Rim et al. in Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
The dataset consists of 4,738 pairs of images of 232 different scenes including reference pairs. All images were captured both in the camera raw and JPEG formats, hence generating two datasets: RealBlur-R from the raw images, and RealBlur-J from the JPEG images. Each training set consists of 3,758 image pairs, while each test set consists of 980 image pairs.
The deblurring result is first aligned to its ground truth sharp image using a homography estimated by the enhanced correlation coefficients method, and PSNR or SSIM is computed in sRGB color space.
Benchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
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RealBlur-R (trained on GoPro)
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ALGNet
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RealBlur-J (trained on GoPro)
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ALGNet
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RealBlur-R
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ALGNet
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RealBlur-J
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ALGNet
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Papers
| Paper | Code | Results | Date | Stars |
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