List of participants of MSU Video Super Resolution Benchmark 2021
| 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| DUF | Yes | Deformable convolution | Unpublished | TensorFlow | 2018 |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 |
| LGFN | Yes | Deformable convolution | Vimeo-90k | PyTorch | 2020 |
| RRN | Yes | Recurrent architecture | Vimeo-90k | PyTorch | 2020 |
| RealSR | No | — | DF2K, DPED | PyTorch | 2020 |
| SOF-VSR | Yes | Optical flow | CDVL | PyTorch | 2020 |
| TDAN | Yes | Deformable convolution | Vimeo-90k | PyTorch | 2020 |
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 aligh neighboring frames.
- Added to the benchmark by: MSU
ESPCN
- 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
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 reccurent strategy with sets of residual blocks to store information from previous frames
- 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 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
See Also
MSU Video Quality Measurement Tool: Picture types
VQMT 13.1 Online help: List of all picture types available in VQMT and their aliases
MSU Video Super Resolution Benchmark
Discover the newest VSR methods and find the most appropriate method for your tasks
MSU VSR Benchmark Methodology
The evaluation methodology of MSU Video Super Resolution Benchmark
MSU SBD Benchmark 2020
MSU SBD Benchmark 2020 Participants
MSU Deinterlacer Benchmark