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02-08-2023, 10:53 AM
ThumperStrauss ThumperStrauss is offline
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I am experimenting with the Real-ESRGAN (by Xintao Wang), which stands for Enhanced Super-Resolution Generative Adversarial Network. This is how the tech is described in a paper:

Quote:
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge.
I'm confused by how to describe what this tool does? It is an upscaler? And what is the relationship between this new tool and the existing toolkit of sharpeners (ex. CAS) and noise reducers (QTGMC)? I don't think ESRGAN is meant to replace those. Is that correct?

I have been experimenting with it within a dev copy of Selur Hybrid (available upon request my IM at Selur's discussion forum). My focus is on 8mm home video tapes, and some VHS second generations of am 8mm tape. I've tried ESRGAN (which has different models inside to choose from). The one I liked best is realesr-general.

There is also another Resizer option in Hybrid called VSGAN, Single Image Super-Resolution Generative Adversarial Network (GAN) which "uses the VapourSynth processing framework to handle input and output image data." I tried a bunch of the models but I think the ESGAN realesr-general was best.

I also compared ESRGAN to Topaz Video AI. Perhaps that is not a fair comparison, because Topaz Video AI doesn't only upscale but does the denoise, sharpening, dehalo and more. But I like to see what Topaz does to my 8mm video (which I deinterlace in Hybrid and upscale to 1080p before importing to Topaz, so that I can get access to Topaz enhanced tools like Proteus and Artemis that aren't available if the video is interlaced). After fiddling with a bunch of settings in Hybrid, I still can exactly replicate what I see in Topaz, which has more to do with my knowledge level on VapourSynth tools than anything with the power of those tools. I guess what I'm saying is that I find myself tempted to buy Topaz because I feel like I will never be able to figure out how to do it is well.

I guess the dream is that one day something much better than Topaz will scan the video and apply the same knowledge and skill that the video restorer experts do and output a truly restored and enhanced copy of 8mm SD footage. If that is the case, I will keep my raw captures safe waiting for that day.
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