esw0116 / dynavsr Goto Github PK

Hi,

Interesting work! I was wondering whether you could share the weights of trained models for the people who are interested in just testing the model. Furthermore, is using the term sub-low-resolution more appropriate than super-low-resolution since the resolution of image you are referring to is lower than the original low-resolution image?

The variables dictionary optim_params for inner_optimizer contains the parameters of modelcp and est_modelcp. However, these two models are not used during the inner update.
Instead, the models model and 'est_model' are used, whether the parameters contained in optim_params should be related to these two models?

I have noticed that the file 'make_downscaled_images.py' doesn't include the name 'vimeo', can I use this file to prepare dataset from vimeo-90k to train? looking for your reply, thank you!

I already have my own 4x dataset to compare model following the REDS structure (and this is using the basicSR VSR loader, so dataloading shouldn't be an issue unless I need to flatten the folders to pretrain SISR), but for the pretraining, I don't know if MFDN needs to pretrain separately before starting a large training session.
Regarding the TOFLOW and EDVR backed models, which networks should be retrained with the pretraining configs when fitting for a new downscale, patch size(depending on the arch), and subject (low noise, computer generated video)?
Secondly, are there other architectures (like RRDB) in this repo that could be used for VSR training? AFAK dcn/EDVR does not pair well with fp16 and AMP training for consumer cards.
Blind VSR seems far more efficient than SOFVSR(which requires deblurring with HINet for satisfactory results) if it can keep flicker low while also upscaling.

Hello,
MSU Video Group has recently launched Video Super Resolution Benchmark and evaluated this algorithm.

DynaVSR-R takes 3rd place by subjective score, 7th place by PSNR, and 3rd by our metric ERQAv1.0. DynaVSR-V takes 13th place by subjective score, 6th place by PSNR, and 10th by our metric ERQAv1.0.
You can see the results here.

If you have any other VSR method you want to see in our benchmark, we kindly invite you to participate.
You can submit it for the benchmark, following the submission steps.

Hi SuYoung,

Can you please comment on how to test DynaVSR on my own dataset without GT?

Thanks:)

I just need to imporve few personal vide files.
Can you please concider to setup your solution in google colab.