tamilventhanrs / convolutional-denoising-autoencoder

EXP NO: 07

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Convolutional Autoencoder for Image Denoising

AIM

To develop a convolutional autoencoder for image denoising application.

Problem Statement and Dataset

Using autoencoder, we are trying to remove the noise added in the encoder part and tent to get the output which should be same as the input with minimal loss. The dataset which is used is mnist dataset.

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:

Add noise factor.

STEP 4:

Create a autoencoder model.

STEP 5:

Compile and fit the created model.

STEP 6:

Display the Original, Noisy and Reconstructed Image.

PROGRAM

Developed by: Tamil Venthan R S
Reg No: 212220230054

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

(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_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()

input_img = keras.Input(shape=(28, 28, 1))

x= layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
layers.MaxPooling2D((2,2),padding='same')(x)
x= layers.Conv2D(14, (3, 3), activation='relu', padding='same')(x)
layers.MaxPooling2D((2,2),padding='same')(x)
x= layers.Conv2D(7, (3, 3), activation='relu', padding='same')(x)

encoded = layers.MaxPooling2D((2, 2), padding='same')(x)

# Encoder output dimension is ## Mention the dimention ##
x= layers.Conv2D(14, (3, 3), activation='relu', padding='same')(encoded)
x=layers.UpSampling2D((2,2))(x)
x=layers.Conv2D(32,(3,3),activation='relu',padding='same')(x)

decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = keras.Model(input_img, decoded)

autoencoder.summary()

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

Original vs Noisy Vs Reconstructed Image

RESULT

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