DnCNN Model
DnCnn model to remove artifacts from the sparseview images [time consuming model].
This project uses Keras API and Tensorflow 1.15 to implement the original DnCNN model. Planning to upgrade code to tensorflow 2.X. Some part of code might be from Google course on Tensorflow with Laurence Moroney. This model takes 10 times more time than UNet model.
The original paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising” can be found at: https://arxiv.org/abs/1608.03981
from google.colab import drive
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly
Enter your authorization code:
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Mounted at /content/drive
To keep using Tensoftlow 1.15 need the code below. By defaut the colab will start using Tnsorflow 2.X
import os
import zipfile
import random
import keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from keras.layers import Concatenate
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Input, Dense
from keras.layers import BatchNormalization, Activation, Dropout, Subtract
from keras.models import Model
from glob import glob
from keras.layers.convolutional import Conv2DTranspose
from keras.layers import concatenate
Using TensorFlow backend.
The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
We recommend you upgrade now
or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:
more info.
# %tensorflow_version 1.x
# import tensorflow as tf
print(tf.__version__)
1.15.0
If we have weights we can downoad weight without training a model. loaded_model.load_weights(“model.h5”)
# loaded_model.load_weights("model.h5")
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/train')))
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/train')))
# 3600
# 3600
3600
3600
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/test/')))
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/test/')))
# # 354
# # 354
354
354
ndct = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/train/*'))
ldct = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/train/*'))
ndct_test = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/test/*'))
ldct_test = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/test/*'))
print(len(ndct))
print(len(ldct))
print(len(ndct_test))
print(len(ldct_test))
3600
3600
354
354
The formulas below will be used to calculate the quality of the reconstruction. Higher PSNR generally indicates high quality of reconstruction.
def cal_psnr(im1, im2):
# assert pixel value range is 0-255 and type is uint8
mse = ((im1.astype(np.float) - im2.astype(np.float)) ** 2).mean()
maxval = np.amax(im1)
psnr = 10 * np.log10(maxval ** 2 / mse)
return psnr
def tf_psnr(im1, im2):
# assert pixel value range is 0-1
#mse = tf.losses.mean_squared_error(labels=im2 * 255.0, predictions=im1 * 255.0)
mse = tf.compat.v1.losses.mean_squared_error(labels=im2 * 255.0, predictions=im1 * 255.0)
return 10.0 * (tf.log(255.0 ** 2 / mse) / tf.log(10.0))
Useing less data: #for i in range(0, 600). Have 3600 in all. Processing 3600 images takes aprox. 20 minutes to run. But once we create .npy aray we don’t have to rerun this code in the future and we will have .npy form of our data. Colab has 11GB RAM limit.
ndct_imgs_train = []
# for i in range(0, len(ndct)):
for i in range(0, 600):
f = open(ndct[i],'rb')
a = np.fromfile(f, np.float32)
ndct_imgs_train.append(a)
f.close()
print("len(ndct_imgs_train)....: ",len(ndct_imgs_train))
#len(ndct_imgs_train)....: 3600
len(ndct_imgs_train)....: 600
Using different range to use less data to train #for i in range(0, 600). In all have 3600 images. It takes aprox. 20 minutes to process all 3600. But once we create .npy aray we don’t have to rerun this code in the future and we will have .npy form of our data.
ldct_imgs_train = []
# for i in range(0, len(ldct)):
for i in range(0, 600):
f = open(ldct[i],'rb')
a = np.fromfile(f, np.float32)
ldct_imgs_train.append(a)
f.close()
print("len(ldct_imgs_train)....: ",len(ldct_imgs_train))
len(ldct_imgs_train)....: 600
only using 100 images to test. In total we have 354 testing images.
ndct_imgs_test = []
# for i in range(0, len(ndct_test)):
for i in range(0, 100):
f = open(ndct_test[i],'rb')
a = np.fromfile(f, np.float32)
ndct_imgs_test.append(a)
f.close()
print("len(ndct_imgs_test)....: ",len(ndct_imgs_test))
len(ndct_imgs_test)....: 100
only using 100 images to test. In total we have 354 images
# load the image
ldct_imgs_test = []
# for i in range(0, len(ldct_test)):
for i in range(0, 100):
f = open(ldct_test[i],'rb')
a = np.fromfile(f, np.float32)
ldct_imgs_test.append(a)
f.close()
print("len(ldct_imgs_test)....: ",len(ldct_imgs_test))
len(ldct_imgs_test)....: 100
Must reshape images to train
ldct_train = np.asarray(ldct_imgs_train)
ndct_train = np.asarray(ndct_imgs_train)
ldct_train = ldct_train.reshape(600,512,512,1)
ndct_train = ndct_train.reshape(600,512,512,1)
ldct_test = np.asarray(ldct_imgs_test)
ndct_test = np.asarray(ndct_imgs_test)
# ldct_test = ldct_test.reshape(len(ldct_imgs_test),512,512,1)
# ndct_test = ndct_test.reshape(len(ldct_imgs_test),512,512,1)
ldct_test = ldct_test.reshape(100,512,512,1)
ndct_test = ndct_test.reshape(100,512,512,1)
print(ldct_train.shape)
print(ndct_train.shape)
print(ldct_test.shape)
print(ndct_test.shape)
(600, 512, 512, 1)
(600, 512, 512, 1)
(100, 512, 512, 1)
(100, 512, 512, 1)
# np.save('sparseview_60_train_600', ldct_train) # save the file as "sparseview_60_train.npy"
# np.save('ndct_train_600', ndct_train) # save the file as "ndct_train.npy"
# np.save('sparseview_60_test_100', ldct_test) # save the file as "sparseview_60_test.npy"
# np.save('ndct_test_100', ndct_test) # save the file as "ndct_test.npy"
np.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_train_600', ldct_train) # save the file as "sparseview_60_train.npy"
np.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/ndct_train_600', ndct_train) # save the file as "ndct_train.npy"
np.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_test_100', ldct_test) # save the file as "sparseview_60_test.npy"
np.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/ndct_test_100', ndct_test) # save the file as "ndct_test.npy"
sparseview_60_train = np.load('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_train_600.npy') # loads saved array into variable sparseview_60_train.
ndct_train = np.load('/content/drive/My Drive/Colab Notebooks/dn_cnn/ndct_train_600.npy') # loads saved array into variable ndct_train.
sparseview_60_test = np.load('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_test_100.npy') # loads saved array into variable sparseview_60_test.
ndct_test = np.load('/content/drive/My Drive/Colab Notebooks/dn_cnn/ndct_test_100.npy') # loads saved array into variable ndct_test.
# sparseview_60_train = np.load('sparseview_60_train_600.npy') # loads saved array into variable sparseview_60_train.
# ndct_train = np.load('ndct_train_600.npy') # loads saved array into variable ndct_train.
# sparseview_60_test = np.load('sparseview_60_test_100.npy') # loads saved array into variable sparseview_60_test.
# ndct_test = np.load('ndct_test_100.npy') # loads saved array into variable ndct_test.
DnCNN model
inputs = Input((None, None,1))
c1 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (inputs)
c2 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c1)
c3 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c2)
c4 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c3)
c5 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c4)
c6 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c5)
c7 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c6)
c8 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c7)
c9 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c8)
c10 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c9)
c11 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c10)
c12 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c11)
c13 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c12)
c14 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c13)
c15 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c14)
c16 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same') (c15)
output_img = Conv2D(filters =1, kernel_size=3, padding='same') (c16)
subtracted = keras.layers.Subtract()([inputs, output_img])
dncnn_model = Model(inputs=[inputs], outputs=[subtracted])
dncnn_model.compile(optimizer='adam', loss='mse', metrics=[tf_psnr])
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/losses/losses_impl.py:121: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
dncnn_model.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None, None, 1 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, None, None, 6 640 input_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, None, None, 6 36928 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, None, None, 6 36928 conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, None, None, 6 36928 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, None, None, 6 36928 conv2d_4[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, None, None, 6 36928 conv2d_5[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, None, None, 6 36928 conv2d_6[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, None, None, 6 36928 conv2d_7[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, None, None, 6 36928 conv2d_8[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, None, None, 6 36928 conv2d_9[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, None, None, 6 36928 conv2d_10[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, None, None, 6 36928 conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, None, None, 6 36928 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, None, None, 6 36928 conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, None, None, 6 36928 conv2d_14[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, None, None, 6 36928 conv2d_15[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, None, None, 1 577 conv2d_16[0][0]
__________________________________________________________________________________________________
subtract_1 (Subtract) (None, None, None, 1 0 input_1[0][0]
conv2d_17[0][0]
==================================================================================================
Total params: 555,137
Trainable params: 555,137
Non-trainable params: 0
__________________________________________________________________________________________________
RAM used: 6 GB GPU used: 15.39 GB
history=dncnn_model.fit(sparseview_60_train, ndct_train, validation_split=0.05, batch_size=10, epochs=20)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
Train on 570 samples, validate on 30 samples
Epoch 1/20
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
570/570 [==============================] - 86s 151ms/step - loss: 0.0014 - tf_psnr: 28.5333 - val_loss: 9.1706e-04 - val_tf_psnr: 30.3876
Epoch 2/20
570/570 [==============================] - 71s 124ms/step - loss: 8.0946e-04 - tf_psnr: 30.9645 - val_loss: 6.6424e-04 - val_tf_psnr: 31.7878
Epoch 3/20
570/570 [==============================] - 71s 124ms/step - loss: 6.0929e-04 - tf_psnr: 32.1629 - val_loss: 5.4812e-04 - val_tf_psnr: 32.6191
Epoch 4/20
570/570 [==============================] - 71s 124ms/step - loss: 5.4067e-04 - tf_psnr: 32.6788 - val_loss: 4.5452e-04 - val_tf_psnr: 33.4299
Epoch 5/20
570/570 [==============================] - 71s 124ms/step - loss: 4.8561e-04 - tf_psnr: 33.1450 - val_loss: 3.7415e-04 - val_tf_psnr: 34.2730
Epoch 6/20
570/570 [==============================] - 71s 124ms/step - loss: 4.5761e-04 - tf_psnr: 33.4051 - val_loss: 3.6532e-04 - val_tf_psnr: 34.3753
Epoch 7/20
570/570 [==============================] - 71s 124ms/step - loss: 4.3427e-04 - tf_psnr: 33.6295 - val_loss: 3.2170e-04 - val_tf_psnr: 34.9267
Epoch 8/20
570/570 [==============================] - 71s 124ms/step - loss: 4.1612e-04 - tf_psnr: 33.8176 - val_loss: 3.0928e-04 - val_tf_psnr: 35.0969
Epoch 9/20
570/570 [==============================] - 71s 124ms/step - loss: 4.0558e-04 - tf_psnr: 33.9281 - val_loss: 3.0073e-04 - val_tf_psnr: 35.2189
Epoch 10/20
570/570 [==============================] - 71s 124ms/step - loss: 3.9466e-04 - tf_psnr: 34.0475 - val_loss: 2.8626e-04 - val_tf_psnr: 35.4328
Epoch 11/20
570/570 [==============================] - 71s 124ms/step - loss: 3.7950e-04 - tf_psnr: 34.2179 - val_loss: 2.8251e-04 - val_tf_psnr: 35.4901
Epoch 12/20
570/570 [==============================] - 71s 124ms/step - loss: 3.7259e-04 - tf_psnr: 34.2983 - val_loss: 2.8502e-04 - val_tf_psnr: 35.4517
Epoch 13/20
570/570 [==============================] - 71s 124ms/step - loss: 3.6003e-04 - tf_psnr: 34.4453 - val_loss: 2.7460e-04 - val_tf_psnr: 35.6132
Epoch 14/20
570/570 [==============================] - 71s 124ms/step - loss: 3.4799e-04 - tf_psnr: 34.5937 - val_loss: 2.7139e-04 - val_tf_psnr: 35.6645
Epoch 15/20
570/570 [==============================] - 71s 124ms/step - loss: 3.4211e-04 - tf_psnr: 34.6718 - val_loss: 2.6640e-04 - val_tf_psnr: 35.7452
Epoch 16/20
570/570 [==============================] - 71s 124ms/step - loss: 3.3954e-04 - tf_psnr: 34.7000 - val_loss: 2.9161e-04 - val_tf_psnr: 35.3525
Epoch 17/20
570/570 [==============================] - 71s 124ms/step - loss: 3.2577e-04 - tf_psnr: 34.8806 - val_loss: 2.5873e-04 - val_tf_psnr: 35.8718
Epoch 18/20
570/570 [==============================] - 71s 124ms/step - loss: 3.1289e-04 - tf_psnr: 35.0590 - val_loss: 2.7251e-04 - val_tf_psnr: 35.6465
Epoch 19/20
570/570 [==============================] - 71s 124ms/step - loss: 3.0929e-04 - tf_psnr: 35.1134 - val_loss: 2.5553e-04 - val_tf_psnr: 35.9259
Epoch 20/20
570/570 [==============================] - 71s 124ms/step - loss: 3.1174e-04 - tf_psnr: 35.0785 - val_loss: 2.4872e-04 - val_tf_psnr: 36.0432
Save weights for the future reuse.
dncnn_model.save_weights("/content/drive/My Drive/Colab Notebooks/dn_cnn/model.h5")
# dncnn_model.load_weights("model.h5")
Plotting PSNR values to see the trend.
import matplotlib.pyplot as plt
acc = history.history['tf_psnr']
val_acc = history.history['val_tf_psnr']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()
plt.show()
<Figure size 432x288 with 0 Axes>
Only trained for 20 epochs as it took 25 mins for 20 epochs.
reconstructed = dncnn_model.predict(sparseview_60_test)
psnr = cal_psnr(ndct_test, reconstructed)
print("psnr 20 epochs.....",psnr)
psnr 20 epochs..... 33.503575808599884
Save images to a folder
from PIL import Image
a = reconstructed[0].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_0.png')
result.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/reconstructed_dncnn_0.png')
a = reconstructed[99].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_99.png')
result.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/reconstructed_dncnn_99.png')
Save original sparseview images to a folder
a = sparseview_60_test[0].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_0.png')
result.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_test_dncnn_0.png')
a = sparseview_60_test[99].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_99.png')
result.save('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_test_dncnn_99.png')
Plot model acuracy
# Plot training & validation accuracy values
plt.plot(history.history['tf_psnr'])
plt.plot(history.history['val_tf_psnr'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
Output images
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_test_dncnn_0.png')
imgplot = plt.imshow(img)
plt.show()
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/dn_cnn/reconstructed_dncnn_0.png')
imgplot = plt.imshow(img)
plt.show()
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/dn_cnn/sparseview_60_test_dncnn_99.png')
imgplot = plt.imshow(img)
plt.show()
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/dn_cnn/reconstructed_dncnn_99.png')
imgplot = plt.imshow(img)
plt.show()
sparseview_60_test_dncnn_0.png
reconstructed_60_test_dncnn_0.png
sparseview_60_test_dncnn_99.png
reconstructed_60_test_dncnn_99.png
