Seungjun Nah

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Ph. D. candidate
Department of ECE, SNU, Seoul, Korea

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Publications | Datasets | CV


REDS dataset

Image

We provide the REalistic and Dynamic Scenes dataset for video deblurring and super-resolution. Train and validation subsets are publicly available. The dataset can be downloaded by running the python code or clicking the links below. Downloads are available via Google Drive and SNU CVLab server.

download_REDS.py

Google Drive

Type Train Validation Test
Sharp train_sharp val_sharp test_sharp
Blur train_blur val_blur test_blur
Blur + Compression train_blur_comp val_blur_comp test_blur_comp
Low Resolution train_sharp_bicubic val_sharp_bicubic test_sharp_bicubic
Blur + Low Resolution train_blur_bicubic val_blur_bicubic test_blur_bicubic

SNU CVLab Server

Type Train Validation Test
Sharp train_sharp val_sharp test_sharp
Blur train_blur val_blur test_blur
Blur + Compression train_blur_comp val_blur_comp test_blur_comp
Low Resolution train_sharp_bicubic val_sharp_bicubic test_sharp_bicubic
Blur + Low Resolution train_blur_bicubic val_blur_bicubic test_blur_bicubic

REDS 120fps

REDS dataset is generated from 120 fps videos, synthesizing blurry frames by merging subsequent frames. The frames that are used to generate blurry images are available below for training and validation data. Due to the large file sizes, the dataset is divided into multiple zip files. Each zip file contains 15 sequences of length 500 that is equivalent to the time duration of the standard 24 fps version above. Each file size is around 10 GB.

Type Google Drive SNU CVLab Server
train_orig_part0.zip link link
train_orig_part1.zip link link
train_orig_part2.zip link link
train_orig_part3.zip link link
train_orig_part4.zip link link
train_orig_part5.zip link link
train_orig_part6.zip link link
train_orig_part7.zip link link
train_orig_part8.zip link link
train_orig_part9.zip link link
train_orig_part10.zip link link
train_orig_part11.zip link link
train_orig_part12.zip link link
train_orig_part13.zip link link
train_orig_part14.zip link link
train_orig_part15.zip link link
val_orig_part0.zip link link
val_orig_part1.zip link link

Updates

Reference

The REDS dataset was used in the NTIRE 2019 and NTIRE 2020 Challenges. If you find our dataset useful for your research, please consider citing our work:

@InProceedings{Nah_2019_CVPR_Workshops_REDS,
  author = {Nah, Seungjun and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},
  title = {NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2019}
}
@InProceedings{Nah_2019_CVPR_Workshops_Deblur,
  author = {Nah, Seungjun and Timofte, Radu and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Lee, Kyoung Mu},
  title = {NTIRE 2019 Challenge on Video Deblurring: Methods and Results},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2019}
}
@InProceedings{Nah_2019_CVPR_Workshops_SR,
	author = {Nah, Seungjun and Timofte, Radu and Gu, Shuhang and Baik, Sungyong and Hong, Seokil and Moon, Gyeongsik and Son, Sanghyun and Lee, Kyoung Mu},
	title = {NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
	month = {June},
	year = {2019}
}
@InProceedings{Nah_2020_CVPR_Workshops_Deblur,
  author = {Nah, Seungjun and Son, Sanghyun and Timofte, Radu and Lee, Kyoung Mu},
  title = {NTIRE 2020 Challenge on Image and Video Deblurring},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2020}
}