Image De-Noising Using Convolutional Variational Autoencoders

Abstract

Abstract: Typically, image noise is random colour information in picture pixels that serves as an unfavourable by-product of the image, obscuring the intended information. In most cases, noise is injected into photographs during the transfer or reception of the image, or during the capture of an image when an object is moving quickly. To improve the noisy picture predictions, autoencoders that denoise the input images are employed. Autoencoders are a sort of unsupervised machine learning that compresses the input and reconstructs an output that is very similar to the original input. The autoencoder tries to figure out non-linear correlations between data points. An encoder, a latent space, and a decoder all exist in autoencoders. The encoder reduces the dimensionality of an original picture to its latent space representation, which is then used by the decoder to reconstruct the reduced dimensional image back to its original image. Basic Autoencoder, Variational Autoencoder, and Convolutional Autoencoder are the three approaches that were employed to denoise the picture. In the basic and convolutional autoencoders, there is only one loss parameter, however in the variational autoencoder, there are two losses: generative loss and latent loss. TensorFlow as the frontend and Keras as the backend are used to implement autoencoders in this project. The noisy pictures are trained on every convolutional variational autoencoder techniques to produce a decent prediction of noisy test data.