xinntao/ESRGAN
1
1
Fork 0
mirror of https://github.com/xinntao/ESRGAN.git synced 2024-08-17 08:30:32 +03:00
Code Issues Projects Releases Wiki Activity

Create QA.md

Browse Source
This commit is contained in:
Xintao 2018-11-08 16:42:42 +08:00 committed by GitHub
parent 0d4fd0fd53
commit 7c80a8def3
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 32 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
QA.md Normal file
View File

@ -0,0 +1,32 @@
# Frequently Asked Questions
### 1. How to reproduce your results in the [PIRM18-SR Challenge](https://www.pirm2018.org/PIRM-SR.html) (with low perceptual index)?
First, the released ESRGAN model in the GitHub (`RRDB_ESRGAN_x4.pth`) is **different** from the model we submitted in the competition.
We found that the lower perceptual index does not always guarantee a better visual quality.
The aims for the competition and our ESRGAN work will be a bit different.
We think the aim for the competition is the lower perceptual index and the aim for our ESRGAN work is the better visual quality.
Therefore, in the PIRM18-SR Challenge competition, we used several tricks for the best perceptual index (see Section 4.5 in the [paper](https://arxiv.org/abs/1809.00219)).
Here, we provid the models and codes used in the competition, which is able to produce the results on the `PIRM test dataset` (we use MATLAB 2016b/2017a):
| Group | Perceptual index | RMSE |
| ------------- |:-------------:| -----:|
| SuperSR | 1.978 | 15.30 |
> 1. Download the model and codes from [GoogleDrive](https://drive.google.com/file/d/1l0gBRMqhVLpL_-7R7aN-q-3hnv5ADFSM/view?usp=sharing)
> 2. Put LR input images in the `LR` folder
> 3. Run `python test.py`
> 4. Run `main_reverse_filter.m` in MATLAB as a post processing
> 5. The results on my computer are: Perceptual index: **1.9777** and RMSE: **15.304**
### 2. How do you get the perceptual index in your ESRGAN paper?
In our paper, we provide the perceptual index in two places.
1). In the Fig. 2, the perceptual index on PIRM self validation dataset is obtained with the **model we submitted in the competition**.
Since the pupose of this figure is to show the perception-distortion plane. And we also use the post-precessing here same as in the competition.
2). In the Fig.7, the perceptual indexs are provided as references and they are tested on the data generated by the released ESRGAN model `RRDB_ESRGAN_x4.pth` in the GiuHub.
Also, there is **no** post-processing when testing the ESRGAN model for better visual quality.