RoopakCS / convolutional-denoising-autoencoder

Convolutional Autoencoder for Image Denoising

AIM

To develop a convolutional autoencoder for image denoising application.

Convolution Autoencoder Network Model

DESIGN STEPS

STEP 1:

Download and split the dataset into training and testing datasets

STEP 2:

rescale the data as that the training is made easy

STEP 3:

create the model for the program , in this experiment we create to networks , one for encoding and one for decoding Write your own steps

PROGRAM

Name : Roopak C S
Reg.No : 212223220088

Importing modules

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import utils
from tensorflow.keras import models
from tensorflow.keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt

Importing Dataset

(x_train, _), (x_test, _) = mnist.load_data()

x_train.shape

x_train_scaled = x_train.astype('float32') / 255.
x_test_scaled = x_test.astype('float32') / 255.
x_train_scaled = np.reshape(x_train_scaled, (len(x_train_scaled), 28, 28, 1))
x_test_scaled = np.reshape(x_test_scaled, (len(x_test_scaled), 28, 28, 1))

noise_factor = 0.5
x_train_noisy = x_train_scaled + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train_scaled.shape) 
x_test_noisy = x_test_scaled + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test_scaled.shape) 

x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)

n = 10
plt.figure(figsize=(20, 2))
for i in range(1, n + 1):
    ax = plt.subplot(1, n, i)
    plt.imshow(x_test_noisy[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

Creating the model

input_img = keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(32,(3,3),activation='relu',padding='same')(input_img)
x = layers.MaxPooling2D((2,2),padding='same')(x)
x = layers.Conv2D(32,(3,3),activation='relu',padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)

x = layers.Conv2D(32,(3,3),activation='relu',padding='same')(encoded)
x = layers.UpSampling2D((2,2))(x)
x = layers.Conv2D(32,(3,3),activation='relu',padding='same')(x)
x = layers.UpSampling2D((2,2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = keras.Model(input_img, decoded)

autoencoder.summary()

Compiling and fitting the model

autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

autoencoder.fit(x_train_noisy, x_train_scaled,epochs=2,batch_size=128,shuffle=True,validation_data=(x_test_noisy, x_test_scaled))

decoded_imgs = autoencoder.predict(x_test_noisy)

n = 10
plt.figure(figsize=(20, 4))
for i in range(1, n + 1):
    # Display original
    ax = plt.subplot(3, n, i)
    plt.imshow(x_test_scaled[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # Display noisy
    ax = plt.subplot(3, n, i+n)
    plt.imshow(x_test_noisy[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)    

    # Display reconstruction
    ax = plt.subplot(3, n, i + 2*n)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

OUTPUT

Training Loss, Validation Loss Vs Iteration Plot

Model Summary:

Original vs Noisy Vs Reconstructed Image

RESULT:

Thus we have successfully developed a convolutional autoencoder for image denoising application.