The original paper is Learning a Deep Convolutional Network for Image Super-Resolution
My implementation have some difference with the original paper, include:
- use 'he_normal' for weight initialization
- use Adam alghorithm for optimization
- I use the opencv library to produce the training data
- I did not set different learning rate in different layer, but I found this network still work.
open prepare_data.py and change the data path to your data
Excute:
python prepare_data.py
Excute:
python main.py
Method: | Bicubic | SRCNN |
---|---|---|
PSNR: | 24.6971375057 | 28.6588428245 |
Origin Image:
Bicubic:
SRCNN: