Jongchan / Tensorflow Vdsr
A tensorflow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", CVPR 16'
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This is a Tensorflow implementation for "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", CVPR 16'.
- The author's project page
- To download the required data for training/testing, please refer to the README.md at data directory.
- VDSR.py : main training file.
- MODEL.py : model definition.
- MODEL_FACTORIZED.py : model definition for Factorized CNN. (not recommended to use. for record purpose only)
- PSNR.py : define how to calculate PSNR in python
- TEST.py : test all the saved checkpoints
- PLOT.py : plot the test result from TEST.py
How To Use
# if start from scratch python VDSR.py # if start with a checkpoint python VDSR.py --model_path ./checkpoints/CHECKPOINT_NAME.ckpt
# this will test all the checkpoint in ./checkpoint directory. # and save the results in ./psnr directory python TEST.py
# plot the psnr result stored in ./psnr directory python PLOT.py
The checkpoint is file is here
Results on Set 5
|2x - PSNR/SSIM||33.66/0.9929||37.53/0.9587||37.24|
|3x - PSNR/SSIM||30.39/0.8682||33.66/0.9213||33.37|
|4x - PSNR/SSIM||28.42/0.8104||31.35/0.8838||31.09|
Results on Set 14
|2x - PSNR/SSIM||30.24/0.8688||33.03/0.9124||32.80|
|3x - PSNR/SSIM||27.55/0.7742||29.77/0.8314||29.67|
|4x - PSNR/SSIM||26.00/0.7027||28.01/0.7674||27.87|
- The training is further accelerated with asynchronous data fetch.
- Tried to accelerate the network with the idea from Factorized CNN. It is possible to implement with
tf.nn.depthwise_conv2dand 1x1 convolution, but not so effective.
- Thanks to @harungunaydin 's comment, AdamOptimizer gives a much more stable training. There's an option added.
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