From June 2022 (note, not to be confused with Video Super-Resolution Transformer, from June 2021, that it improves upon):
See pictures (there and in the paper) and:
Following commands will download pretrained models and test datasets automatically
[…]
We achieved state-of-the-art performance on video SR, video deblurring and video denoising. Detailed results can be found in the paper.
outperforms the state-of-the-art methods by large margins (up to 2.16dB) on fourteen benchmark datasets
VRT achieves state-of-the-art performance on video restoration, including video super-resolution, deblurring, denoising, frame interpolation and space-time video super-resolution.
reducing the computational complexity to O(T). […]
In contrast, VRT performs well on both short and long sequences.
[…]
4.3. Video Deblurring
[…] It is clear that VRT achieves the best performance, outperforming the second best method ARVo by a remarkable improvement of 1.47dB and 0.0299 in terms of PSNR and SSIM. […]
The runtime is 2.2s per frame on 1280 × 720 blurred videos. Notably, during evaluation, we do not use any pre-processing techniques such as sequence truncation and image alignment […]
4.4. Video Denoising
[…] Even though PaCNet [93] trains different models separately for different noise levels, VRT still improves the PSNR by 0.82∼2.16dB.4.5. Video Frame Interpolation
To show the generalizability of our framework, we conduct experiments on video frame interpolation. […] RT achieves best or competitive performance on all datasets compared with it competitors, including those using depth maps or optical flows. As for the model size, VRT only has 9.9M parameters, which is much smaller than the recent best model FLAVR (42.4M).
From the same people (previously state-of-the-art since from August 2021, but “JPEG compression artifact reduction” intriguing, and unclear if (their above) video de-noising code handles compressed video, and does something for analogous blocking artefacts):
We conduct experiments on three represen-
tative tasks: image super-resolution (including classical,
lightweight and real-world image super-resolution), image
denoising (including grayscale and color image denoising)
and JPEG compression artifact reduction. Experimental re-
sults demonstrate that SwinIR outperforms state-of-the-art
methods on different tasks by up to 0.14∼0.45dB, while the
total number of parameters can be reduced by up to 67%.
See also (now 3rd on quality, and fastest?):
Fastdvdnet: Towards real-time deep video denoising without flow estimation.
FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times.
featuring very fast running times—even thousands of times faster than other relevant methods.
Runtime is 0.1 sec. so not too far from real-time, hopefully can be improved, or just wait a short while until hardware makes up for the difference:
Web-demo here (upload photo or use your web camera):