MSU Video Deblurring Benchmark

The most comprehensive comparison of video deblurring methods

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G&M Lab head: Dr. Dmitriy Vatolin
Measurements, analysis: 
Nikita Alutis,
Egor Chistov
Consultant: Mikhail Dremin

Key features of the Benchmark

  • A new private real motion blur dataset for testing: 23 different scenes
  • Comparison of 9 methods of video deblurring: VRT, NAFNet and more
  • 5 metrics for restoration quality assessment: PSNR, SSIM, VMAF and more
  • (Soon) Subjective comparison

What’s new

  • 24.10.2022 Beta-version release.


Click on the labels to sort the table.
In the methodology you can read brief information about all metrics.
You can scroll the table to see all the results.

1 NAFNet (REDS*) 30.54803 0.95035 66.85941 0.08561 0.74508
2 MAXIM (REDS*) 30.65728 0.94959 67.3502 0.07836 0.74277
3 Restormer 31.76111 0.94632 66.3964 0.08239 0.74776
4 VRT (REDS*) 30.97878 0.94601 66.81782 0.08248 0.75056
5 MPR local 31.65037 0.94542 67.01788 0.08323 0.74521
6 VRT (GoPro*) 31.42945 0.94503 66.72253 0.08165 0.74874
7 Deeprft (GoPro*) 31.57612 0.94484 66.55057 0.08326 0.74323
8 Deeprft (REDS*) 31.32349 0.94479 66.46811 0.08139 0.74339
9 MAXIM (GoPro*) 31.36344 0.94386 67.7557 0.09188 0.74444
10 DeblurGAN Inception 31.17171 0.94301 66.91781 0.08867 0.74297
11 Restormer local 31.12341 0.94217 65.25911 0.08251 0.73875

* Train dataset


In this section you can observe the values of different metrics for each individual video and the average values. In the "Video" selector, you can observe the names of the videos. For your convenience, the methodology page has a preview of each video and its full name match.

Metric: Video:


This section presents visualizations of all algorithms.

  • The first line is full-sized frames
  • The second line is crops from them
  • The third and fourth lines are the visualizations of error maps of PSNR and SSIM respectively
You can select up to three models for comparison and one of the test videos. Use sliding window to zoom ROI and better consider deblurring artifacts.

Note that only 5 videos from dataset are publicly available now.


Model 1: Model 2: Model 3:

Drag a red rectangle in the area, which you want to crop.





Deblurring Benchmark Roadmap

Feature Description Release date
Subjective comparison We plan to conduct a subjective comparison. The subjective comparison with many video pairs may be very expensive. If you want to support our benchmark, please contact us: Q4 2022
More state-of-the-art deblurring methods New deblurring methods are constantly being developed. We will add new qualitative deblurring methods to our benchmark as they appear. We also expect developers to submit their methods to us. You can submit your method here. Q4 2022
Paper on benchmark/dataset We plan to publish a paper on the presented benchmark and futhermore explain our methodology Q1 2023
A new metric to measure video deblurring restoration quality We believe that the most popular video quality metrics — PSNR and SSIM — are not applicable to the deblurring task. We are researching our metric for blurred video restoration quality that will correlate well with subjective scores. Q2 2023

Submit Your Method

Verify your method’s ability to restore blurred videos and compare it with other algorithms.
You can go to the page with information about other participants.

1. Download input data
Download blurred videos
2. Apply your algorithm Deblur videos using your algorithm.
You can also send us the code of your method or the executable file and we will run it ourselves.
3. Send us result Send us an email to with the following information:
    A. Name of your method that will be specified in our benchmark
    B. Link to the cloud drive (Google Drive, OneDrive, Dropbox, etc.), containing output frames
      You can send us files in the following formats:
      1) .png, .tif, if your method outputs images
      2) .mov, .mp4 if you make a video of the frames yourself
      Please read the evaluation section of the methodology before submitting your algorithm
    C. (Optional) Execution time of your algorithm and information about used GPU
    D. (Optional) Any additional information about the method:
      1. Full name of your model
      2. The parameter set that was used
      3. A link to the code of your model, if it is available
      4. A link to the paper about your model, if it is available
      5. Any other additional information

You can verify the results of current participants or estimate the perfomance of your method on public samples of our dataset. Just send an email to with a request to share them with you.

Our policy: we share only public samples of our dataset

Contact Us

For questions and propositions, please contact us:

You can subscribe to updates on our benchmark:

MSU Video Quality Measurement Tool


    The tool for performing video/image quality analyses using reference or no-reference metrics

Widest Range of Metrics & Formats

  • Modern & Classical Metrics SSIM, MS-SSIM, PSNR, VMAF and 10+ more
  • Non-reference analysis & video characteristics
    Blurring, Blocking, Noise, Scene change detection, NIQE and more

Fastest Video Quality Measurement

  • GPU support
    Up to 11.7x faster calculation of metrics with GPU
  • Real-time measure
  • Unlimited file size

  • Main MSU VQMT page on

24 Oct 2022
See Also
Learning-Based Image Compression Benchmark
The First extensive comparison of Learned Image Compression algorithms
Super-Resolution Quality Metrics Benchmark
Discover 66 Super-Resolution Quality Metrics and choose the most appropriate for your videos
Video Saliency Prediction Benchmark
Explore the best video saliency prediction (VSP) algorithms
Super-Resolution for Video Compression Benchmark
Learn about the best SR methods for compressed videos and choose the best model to use with your codec
Metrics Robustness Benchmark
Check your image or video quality metric for robustness to adversarial attacks
Video Upscalers Benchmark
The most extensive comparison of video super-resolution (VSR) algorithms by subjective quality
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