tensorflow-vdsr

Overview

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.

Files

• 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

Training

# if start from scratch
python VDSR.py
# if start with a checkpoint
python VDSR.py --model_path ./checkpoints/CHECKPOINT_NAME.ckpt

Testing

# this will test all the checkpoint in ./checkpoint directory.
# and save the results in ./psnr directory
python TEST.py

Plot Result

# plot the psnr result stored in ./psnr directory
python PLOT.py

Result

The checkpoint is file is here

Results on Set 5

Scale	Bicubic	VDSR	tf_VDSR
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

Scale	Bicubic	VDSR	tf_VDSR
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

Remarks

• 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_conv2d and 1x1 convolution, but not so effective.
• Thanks to @harungunaydin 's comment, AdamOptimizer gives a much more stable training. There's an option added.