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base repository: igv/FSRCNN-TensorFlow
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head repository: HelpSeeker/FSRCNN-TensorFlow
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- 10 commits
- 8 files changed
- 2 contributors
Commits on Oct 1, 2019
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Increase artifact mitigation (--distort)
The current compression artifact mitigation via --distort is far to weak to enhance low quality footage. The chance to degrade a training image is too low and the quality range too high, as to make a noticeable difference. After quite a few tests these settings seem to achieve a good balance between efficient artifact removal and the famous oil painting effect.
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Decrease verbosity to a minimum
Suppresses all Future Warnings and reduces Tensorflow verbosity to basically nothing. Feels a bit hacky in main.py and suppressing warnings is usually not the brightest thing to do, but I don't have the knowledge to fix any major Tensorflow breakage anyway. On the bright side you can now easily look up information printed by the scripts, without having to weed through walls of text (especially useful, if you run several short training sessions in succession).
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Commits on Oct 2, 2019
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Commits on Oct 3, 2019
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Merge pull request haasn#1 from igv/master
Pull recent igv commits (2019-10-02)
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Commits on Oct 4, 2019
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Merge pull request #2 from igv/master
Pull recent igv commits (2019-10-04)
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Commits on May 11, 2023
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Found some old comparison images that I made back in the day. Might as well include them. They showcase what kind of source quality I had in mind while creating this project.
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