List of participants of MSU Video Super-Resolution Benchmark: Detail Restoration
Name | Multi-frame | Integrate temporal information by | Training dataset | Framework | Year |
---|---|---|---|---|---|
D3Dnet | Yes | Deformable convolution | Vimeo-90k | PyTorch | 2020 |
DBVSR | Yes | Optical flow | REDS | PyTorch | 2020 |
DUF | Yes | Deformable convolution | Unpublished | TensorFlow | 2018 |
DynaVSR-V | Yes | Vimeo-90k | PyTorch | 2020 | |
DynaVSR-R | Yes | REDS | PyTorch | 2020 | |
ESPCN | No | — | ImageNet | PyTorch | 2016 |
ESRGAN | No | — | PyTorch | 2018 | |
iSeeBetter | Yes | Recurrent architecture + optical flow | PyTorch | 2020 | |
LGFN | Yes | Deformable convolution | Vimeo-90k | PyTorch | 2020 |
RBPN | Yes | Recurrent architecture + optical flow | Vimeo-90k | PyTorch | 2019 |
RRN | Yes | Recurrent architecture | Vimeo-90k | PyTorch | 2020 |
Real-ESRGAN | No | — | PyTorch | 2021 | |
Real-ESRNet | No | — | PyTorch | 2021 | |
RealSR | No | — | DF2K, DPED | PyTorch | 2020 |
RSDN | Yes | Recurrent | Vimeo-90k | PyTorch | 2020 |
SOF-VSR | Yes | Optical flow | CDVL | PyTorch | 2020 |
TDAN | Yes | Deformable convolution | Vimeo-90k | PyTorch | 2020 |
TGA | Yes | Temporal Group Attention | Vimeo-90k | PyTorch | 2020 |
TMNet | Yes | Deformable convolution | Vimeo-90k | PyTorch | 2021 |
D3Dnet
- Use a deformable 3D convolution to compensate the motion between frames implicitly.

- Added to the benchmark by MSU
DBVSR
- Estimate a motion blur for the particular input. Compensate the motion between frames explicitly.

- Added to the benchmark by MSU
DUF
- Two models: DUF-16L (16 layers), DUF-28L (28 layers)
- Use a deformable 3D convolution to compensate the motion between frames implicitly.

- Added to the benchmark by MSU
DynaVSR
- Use meta-learning to estimate a degradation kernel for the particular input.
- DynaVSR can be applied to any VSR deep-learning model. For our benchmark, we used pretrained weights for model EDVR, which use Deformable convolution to align neighboring frames.

- Added to the benchmark by MSU
ESPCN

- Added to the benchmark by MSU
ESRGAN

- Added to the benchmark by MSU
iSeeBetter

- Added to the benchmark by MSU
LGFN
- Use deformable convolutions with decreased multi-dilation convolution units (DMDCUs) to align frames explicitly. Fuse features from local and global fusion modules.

- Added to the benchmark by MSU
RBPN

- Added to the benchmark by MSU
Real-ESRGAN
- Added to the benchmark by MSU
Real-ESRNet
- Added to the benchmark by MSU
RealSR
- Try to estimate degradation kernel and noise distribution for better visual quality.

- Added to the benchmark by MSU
RRN
- Two models: RRN-5L (five residual blocks), RRN-10L (ten residual blocks)
- Use recurrent strategy with sets of residual blocks to store information from previous frames.

- Added to the benchmark by MSU
RSDN
- It divides the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks.

- Added to the benchmark by MSU
SOF-VSR
- Two models: SOF-VSR-BD (trained on gauss degradation type), SOF-VSR-BI (trained on bicubic degradation type)
- Compensate motion by high-resolution optical flow, estimated from the low-resolution one in a coarse-to-fine manner.

- Added to the benchmark by MSU
TDAN
- Use a deformable 3D convolution to compensate the motion between frames implicitly.

- Added to the benchmark by MSU
TGA
- The input sequence is reorganized into several groups of subsequences with different frame rates. The grouping allows to extract spatio-temporal information in a hierarchical manner, which is followed by an intra-group fusion module and inter-group fusion module.

- Added to the benchmark by MSU
TMNet
- Temporal Modulation Network was trained for Space-Time Video Super Resolution. The temporal information is integrated by deformable convolution with the multi-frame input.

- Added to the benchmark by the author, Gang Xu.
See Also
VQA Dataset
During our work we have created the database for video quality assessment with subjective scores
MSU Video Upscalers Benchmark 2022
The most extensive comparison of video super-resolution (VSR) algorithms by subjective quality
MSU Video Deblurring Benchmark 2022
Learn about the best video deblurring methods and choose the best model
Site structure
-
MSU Benchmark Collection
- MSU Video Upscalers Benchmark 2022
- MSU Video Deblurring Benchmark 2022
- MSU Video Frame Interpolation Benchmark 2022
- MSU HDR Video Reconstruction Benchmark 2022
- MSU Super-Resolution for Video Compression Benchmark 2022
- MSU No-Reference Video Quality Metrics Benchmark 2022
- MSU Full-Reference Video Quality Metrics Benchmark 2022
- MSU Video Alignment and Retrieval Benchmark
- MSU Mobile Video Codecs Benchmark 2021
- MSU Video Super-Resolution Benchmark
- MSU Shot Boundary Detection Benchmark 2020
- MSU Deinterlacer Benchmark
- The VideoMatting Project
- Video Completion
- Codecs Comparisons & Optimization
- VQMT
- MSU Datasets Collection
- Metrics Research
- Video Quality Measurement Tool 3D
- Video Filters
- Other Projects