Image-Denoising-AE

Image Denoising using AutoEncoders in Keras

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Learning Objectives

• Understand the theory and intuition behind Autoencoders
• Import Key libraries, dataset and visualize images
• Perform image normalization, pre-processing, and add random noise to images
• Build an Autoencoder using Keras with Tensorflow 2.0 as a backend
• Compile and fit Autoencoder model to training data
• Assess the performance of trained Autoencoder using various KPIs

Walkthrough

• Import libraries and datasets
• Perform data visualization
• Perform data preprocessing
• Understand the theory and intuition behind autoencoders
• Build and train autoencoder model
• Evaluate trained model performance

Approach

• We need both the noisy image and the clean image for training the DAE.
• The noisy image could be synthesized by adding gaussian noise to the original input images.
• The noisy images would be fed to the DAE and the output ie. the reconstructed input will be compared with the clean image.
• The objective of the DAE is to minimize the reconstruction error which is the difference between the clean input image and the generated output image.

Note:

• While training the DAE (model.fit(...)) pass the noisy image as the input but in the ouput part pass the clean version ie. the original input image.
• The reconstruction error is between the generated output and the clean input image.
• This will ensure that given the noisy input, the DAE will produce a denoised version of the image by extracting important features in the coding bottleneck layer.

Basic Model Output after 10 epochs

FMNIST Dataset

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.