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Old 30th October 2023, 21:49   #21  |  Link
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Thanks yeah I just threw it out there since it was temporal but still hoping for the TempForm with overlap loss to be available soon. For now I have tried vs-Scunet and Dpir (only in HD) on noisy uhd but I was disappointed and its just too slow even with my new setup. Mdegrain became a real joy though.

Last edited by anton_foy; 30th October 2023 at 22:05.
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Old 31st January 2024, 17:35   #22  |  Link
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For those who are interested in, we've collected many "AI" (= GANs-based) denoisers here: https://github.com/forart/HyMPS/blob....md#denoisers-
Hybrid Multimedia Production Suite will be a platform-indipendent open source suite for advanced audio/video contents production.

Official git: https://www.forart.it/HyMPS/
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Old 1st February 2024, 09:43   #23  |  Link
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At first it is nice to see such active new ideas production for denoising. At least after 2010 year. But after next look I see the marks 'last commits 2..4+ years ago' after all listed projects. All ideas do not go into finally working implementations for current public motion pictures data processing environments like Avisynth/Vapoursynth or ffmpeg.

It looks like most of these 'tech demos' are in the 'waiting for investor' state ? Or were developed only to make publication at IEEE/ICIP and put these tech demo sources at github for hosting ?

Unfortunately sometimes a project only designed to make publication and rating increase to authors at the graduation but not to make a practically useful product. It is typically started with 'based on the IEEE publication...' and ended in the 'working math demo in matlab'. We need some more steps to make 'engineers build' as compiled and optimized for performance software for today's end-users CPUs and GPUs to test and use in real encodings. These steps require practical programmers resources and looks like original developers of the new ideas do not have any profit from finishing some practically useful software.

Originally Posted by poisondeathray View Post
And you really need to train your own models for the best results
It may be good for 'offline' moving pictures production. So the production team can make shots of talents with good lighting conditions and low camera noise to 'train' models and next apply such processing to noised shots in bad lighting and high camera noise.

But for 'blind' denoising without previously known clean and perfect image data it may be not applicable.

This processing workflow is close to 2 steps:
1. Create library of clean image blocks
2. Detects the most likely looking image block in the current noised frame and replaces it with a clean view from the library.

For 'blind' denoise of long movies (of standard runtime 1 hr 30 min or more) it is possible to 'train' model on the all presented views of talent (any subject) in all frames and make 'library of clean views'.
And at the second pass replace bad noised image blocks with transformed clean blocks (applying all required and supported transforms of lighting and geometry). Implementation of such 2 pass denoising can be also 'very slow'.

It means finally expanding 'temporal size' of denoiser to the total available movie runtime (all frames). It is possible to model this denoiser using current mvtools project sources too. But for temporal 'cleaned blocks' storage may require lots of HDD space (max expected is RGB24 uncompressed frames size). Also running MAnalyse with tr about total_num_frames/2 may be too slow. So this approach requires reduction of block search steps. Currently to make implementation of MAnalyse+MDegrain more simple we still have lots of redundant search operations (this makes quality better but takes more computing resources). Also the quality of noise reduction will be more variable over the cutscenes and each subject - the more time it appears in the total movie the better its views are cleaned. Rare subjects like 1..2 frames views for total movie can not be denoised in such a way.

Last edited by DTL; 1st February 2024 at 10:09.
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