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2021-07-25 02:11:54 +08:00
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README.md
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Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
:triangular_flag_on_post: The training codes have been released. A detailed guide will be provided later (on July 25th). Note that the codes have a lot of refactoring from our developed codes. So there may be some bugs/performance drops. Welcome to report issues and I wil also retrain the models.
:triangular_flag_on_post: The training codes have been released. A detailed guide will be provided later (on July 25th). Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report issues and I wil also retrain the models.
### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
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Here is a TODO list in the near future:
[ ] optimize for human faces
[ ] optimize for texts
[ ] optimize for animation images
[ ] support more scales
[ ] support controllable restoration strength
- [ ] optimize for human faces
- [ ] optimize for texts
- [ ] optimize for animation images
- [ ] support more scales
- [ ] support controllable restoration strength
If you have any good ideas or demands, please open an issue/discussion to let me know. <br>
If you have some images that Real-ESRGAN could not well restored, please also open an issue/discussion. I will record it (but I cannot guarantee to resolve it:stuck_out_tongue:). If necessary, I will open a page to specially record these real-world cases that need to be solved, but the current technology is difficult to handle well.