Image Super-Resolution Using Very Deep Residual Channel Attention Networks Implementation in Tensorflow
This repo contains my implementation of RCAN (Residual Channel Attention Networks).
Here're the proposed architectures in the paper.
Channel Attention (CA)
Residual Channel Attention Block (RCAB)
Residual Channel Attention Network (RCAN), Residual Group (GP)
All images got from the paper
DataSet | LR | HR |
---|---|---|
DIV2K | 800 (192x192) | 800 (768x768) |
# hyper-paramters in config.py, you can edit them!
$ python3 train.py --data_from [img or h5]
$ python3 test.py --src_image sample.png --dst_image sample-upscaled.png
Example\Resolution | 192x192x3 image (sample) | 768x768x3 image (generated) |
---|---|---|
Example1 (X4 scaled) |
HyeongChan Kim / @kozistr