Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ...

However, existing sub-aperture based light field cameras are limited by their sensor resolution to obtain high spatial and angular resolution images simultaneously. In this paper, we propose an ...

A good way to see where this article is headed is to take a look at the screenshot of a demo program in Figure 1. The demo analyzes a dataset of 3,823 images of handwritten digits where each image is ...

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two codes. The structure code is a tensor with spatial ...

Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation. Top: An encoder E embeds an input (Notre-Dame) into two codes. The structure code is a tensor with spatial ...

Emerging deep learning approaches have facilitated image reconstruction at the expense of excessive model complexities and lack of theoretical guarantees of stability. In this paper, we propose a ...

[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 ...

In this article, we will demonstrate the implementation of a Deep Autoencoder in PyTorch for reconstructing images. This deep learning model will be trained on the MNIST handwritten digits and it will ...