TensorFlow-SRDenseNet
Introduction
We present a highly accurate single-image super-resolution (SR) method, Use the DenseNet, and use deconvulotion to scaling, the network model of densenet is:
def desBlock(self, desBlock_layer, outlayer, filter_size=3 ):
nextlayer = self.low_conv
conv = list()
for i in range(1, outlayer+1):
conv_in = list()
for j in range(1, desBlock_layer+1):
# The first conv need connect with low level layer
print(i,j)
if j is 1:
x = tf.nn.conv2d(nextlayer, self.weight_block['w_H_%d_%d' %(i, j)], strides=[1,1,1,1], padding='SAME') + self.biases_block['b_H_%d_%d' % (i, j)]
x = tf.nn.relu(x)
conv_in.append(x)
else:
x = Concatenation(conv_in)
x = tf.nn.conv2d(x, self.weight_block['w_H_%d_%d' % (i, j)], strides=[1,1,1,1], padding='SAME')+ self.biases_block['b_H_%d_%d' % (i, j)]
x = tf.nn.relu(x)
conv_in.append(x)
nextlayer = conv_in[-1]
print(conv_in[-1])
conv.append(conv_in)
print(conv)
return convDependency
pip
- TensorFlow
- OpenCV
- h5py
Environment
- Ubuntu 16.04
- Python 2.7
If you meet the problem with opencv when run the program
libSM.so.6: cannot open shared object file: No such file or directory
please install dependency package
sudo apt-get install libsm6
sudo apt-get install libxrender1
All Parameter
usage: main.py [-h] [--epoch EPOCH] [--image_size IMAGE_SIZE]
[--label_size LABEL_SIZE] [--c_dim C_DIM]
[--is_train [IS_TRAIN]] [--nois_train] [--scale SCALE]
[--stride STRIDE] [--checkpoint_dir CHECKPOINT_DIR]
[--learning_rate LEARNING_RATE] [--batch_size BATCH_SIZE]
[--des_block_H DES_BLOCK_H] [--des_block_ALL DES_BLOCK_ALL]
[--result_dir RESULT_DIR] [--growth_rate GROWTH_RATE]
[--test_img TEST_IMG]
optional arguments:
-h, --help show this help message and exit
--epoch EPOCH Number of epoch
--image_size IMAGE_SIZE
The size of image input
--label_size LABEL_SIZE
The size of label
--c_dim C_DIM The size of channel
--is_train [IS_TRAIN]
if the train
--nois_train
--scale SCALE the size of scale factor for preprocessing input image
--stride STRIDE the size of stride
--checkpoint_dir CHECKPOINT_DIR
Name of checkpoint directory
--learning_rate LEARNING_RATE
The learning rate
--batch_size BATCH_SIZE
the size of batch
--des_block_H DES_BLOCK_H
the size dense_block layer number
--des_block_ALL DES_BLOCK_ALL
the size dense_block
--result_dir RESULT_DIR
Name of result directory
--growth_rate GROWTH_RATE
the size of growrate
--test_img TEST_IMG test_img
if you want to see the flag
python main.py -h
How to train
python main.py
How to test
python main.py --is_train False --stride 50
If you want to Test your own iamge
use test_img flag
python main.py --is_train False --stride 50 --test_img Train/t20.bmp
then result image also put in the result directory
Result
-
Origin
-
Bicbuic
-
Result
Because the stride is 50, some part are cut.