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 convpip
- TensorFlow
- OpenCV
- h5py
- 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 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 python main.py 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
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Origin
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Bicbuic
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Result
Because the stride is 50, some part are cut.