[Click on image for larger view.] Figure 1: Autoencoder Anomaly Detection in Action This article assumes you have an intermediate or better familiarity with a C-family programming language, preferably ...

This project implements a convolutional autoencoder for image denoising using the MNIST handwritten digit dataset. The autoencoder learns to remove artificially added noise from digit images, ...

Owing to the immense popularity of ray-tracing and path tracing rendering algorithms for visual effects, there has been a surge of interest in developing filtering and reconstruction methods to deal ...

This proposed study is focusing on introducing a novel approach to detect and deblur motion-blurred images using autoencoder mechanism. This proposed study will be a remarkable era in image quality ...

This This article introduces a novel approach to image compression through the utilization of autoencoders, a class of neural networks adept at learning to distill an image's essential attributes and ...

Based on these observations, we develop a boundary and mask adversarial learning based on the convolutional autoencoder method to segment the OD and OC from the target domain fundus images by ...

This project aims to demonstrate whether an autoencoder can be used to encrypt images. An autoencoder is a type of neural network that learns to compress and then reconstruct data. In this case, we ...

Keywords: hyperspectral images, spectral unmixing, endmembers, abundance maps, image processing, deep learning, autoencoder, algal bloom Citation: Alfaro-Mejía E, Manian V, Ortiz JD and Tokars RP ...