We need the following libraries for Denoising of image:
from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Sequential
from keras import backend as K
First we have to load the data (here we load data from MNIST Database)
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
#We need the following libraries for Denoising of image:
from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Sequential
from keras import backend as K
#First we have to load the data (here we load data from MNIST Database)
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
Removing unwanted noise in order to restore the original image.
minify code