Autoencoder_Denoise-

Removing Noise from Images Using Autoencoders

Overview

This project explores autoencoders, a class of neural networks used for unsupervised learning. Specifically, we focus on using autoencoders to remove noise from images and restore corrupted images. We will implement different types of autoencoders and analyze their effectiveness in denoising tasks.

Topics Covered

• Introduction to Autoencoders
• Unsupervised Learning with Autoencoders
• Types of Autoencoders:
  • Basic Autoencoders
  • Deep Autoencoders
  • Convolutional Autoencoders
• Applications:
  • Image Compression
  • Image Denoising
• Step-by-Step Guide to Building and Training an Autoencoder in Keras
• Analysis of Results

Technical Requirements

To run the code in this project, ensure you have the following dependencies installed:

Library	Version
matplotlib	3.0.2
Keras	2.2.4
numpy	1.15.2
PIL	5.4.1

Getting Started

To get started with the project, clone the repository and set up a virtual environment:

Step 1: Clone the Repository

$ git clone https://github.com/PacktPublishing/Neural-Network-Projects-with-Python.git
$ cd Neural-Network-Projects-with-Python

Step 2: Set Up the Virtual Environment (Using Conda)

Ensure you have Anaconda installed, then run:

$ conda env create -f environment.yml
$ conda activate neural-network-projects-python

Step 3: Navigate to the Chapter 5 Directory

$ cd Chapter05

Project Files

File Name	Description
autoencoder_image_compression.py	Code for building a simple autoencoder for image compression.
basic_autoencoder_denoise_MNIST.py	Basic autoencoder for denoising MNIST images.
conv_autoencoder_denoise_MNIST.py	Convolutional autoencoder for denoising MNIST images.
basic_autoencoder_denoise_documents.py	Basic autoencoder for denoising document images.
deep_conv_autoencoder_denoise_documents.py	Deep convolutional autoencoder for denoising document images.

Running the Code

To execute a specific script, use:

$ python <script_name>.py

For example, to run the image compression autoencoder:

$ python autoencoder_image_compression.py

Results and Analysis

The trained autoencoders can effectively remove noise from images, demonstrating their utility in real-world applications like image enhancement, medical imaging, and document restoration.