Implement of ESRGAN(https://arxiv.org/abs/1809.00219) with Keras
It was programed based on https://github.com/MathiasGruber/SRGAN-Keras, and refered from https://github.com/SavaStevanovic/ESRGAN
It is not finished totally, there are several bugs, but you can try to run ESRGAN_demo.py to have a test.
If you want to initially train a pleasant result by yourself, you'd better use 'RTC-SR.py' for a moment.
I have uploaded the weights of my generator model(RRDB). You can use it after copying my generator model code. If you don't copy the model code, it may report some errors beacause I used 'tf.xxx' in my model.
To be honest, I recommand you to train your own discriminator due to the discriminator has several versions, and I'm not sure which one is better. You can add a Dropout(0.4) layer at the last of the discriminator to keep the training process stable (you'd better gradually decrease it to zero at last).
My Implement now doesn't use RaGAN due to bugs.
Thanks @Zeyu_Lian for telling me that I forgot add K.sigmoid() in ESRGAN.py(line 322~323). What's more, he pointed out that "The last layer of the generator show have used filter with kernal size 9, not 3." However, I'm suffering with my final exam(都是鸽的借口）, so I will try to fix bugs and test them later.
What's more, I use DIV2K datasets only. After doing experiments, I'm sure that "the more high quality data, the better model performance" is TRUE.
the examples of my ESRGAN (without RaGAN)
Don't mind the name of the third subtitle 'SRGAN'. It should be 'ESRGAN' (I forgot to change at that time).
The next two figures show how ESRGAN directly super-resolute the actual natural image. These two images are cropped from the original image of the DIV2K dataset. There is no "super-resolution original image" that can be compared, so their effects can truly reflect the actual application effect of super-resolution, rather than the reconstruction effect.
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Other pictures' PSNR and SSIM are higher than these, but I think it is more clear. Don't mind it too much if you don't want to use it on security field and medical field.
- I deleted the Flatten() to achieve a FCN structure, and it is equal to use GlobalAveragePooling(). It is ok for the discriminator in order to use a progressive training method, which is more stable and efficient.
- You'd better add SN layer in D(actually G is ok, too) to optimize the stability while training(I use it in RTC_SR.py). For the details, you can read https://kexue.fm/archives/6051#Keras%E5%AE%9E%E7%8E%B0.