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► Code examples / Computer Vision / Convolutional autoencoder for image denoising

Convolutional autoencoder for image denoising

Author: Santiago L. Valdarrama
Date created: 2021/03/01
Last modified: 2021/03/01
Description: How to train a deep convolutional autoencoder for image denoising.

ⓘ This example uses Keras 3

View in Colab • GitHub source

Introduction

This example demonstrates how to implement a deep convolutional autoencoder for image denoising, mapping noisy digits images from the MNIST dataset to clean digits images. This implementation is based on an original blog post titled Building Autoencoders in Keras by François Chollet.

Setup

import numpy as np
import matplotlib.pyplot as plt

from keras import layers
from keras.datasets import mnist
from keras.models import Model


def preprocess(array):
    """Normalizes the supplied array and reshapes it."""
    array = array.astype("float32") / 255.0
    array = np.reshape(array, (len(array), 28, 28, 1))
    return array


def noise(array):
    """Adds random noise to each image in the supplied array."""
    noise_factor = 0.4
    noisy_array = array + noise_factor * np.random.normal(
        loc=0.0, scale=1.0, size=array.shape
    )

    return np.clip(noisy_array, 0.0, 1.0)


def display(array1, array2):
    """Displays ten random images from each array."""
    n = 10
    indices = np.random.randint(len(array1), size=n)
    images1 = array1[indices, :]
    images2 = array2[indices, :]

    plt.figure(figsize=(20, 4))
    for i, (image1, image2) in enumerate(zip(images1, images2)):
        ax = plt.subplot(2, n, i + 1)
        plt.imshow(image1.reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().