(Image source Keras) . An autoencoder is a neural network that is trained to attempt to copy its input to its output. Internally, it has a hidden layer h that describes a code used to represent the ...

In this code, we perform Image Denoising on a standard MNIST Digits Dataset, which comprises of 28 x 28 pixel images of 0-9 digits. First we'll add Salt and Pepper noise to our dataset and then define ...

To ensure a fair comparison with Mostafa’s proposed CAE model, we maintained consistency in data processing, using T1-weighted MRI slice images of the healthy control group for autoencoder ...

Abstract: This paper presents a deep learning-based pansharpening method for fusion of panchromatic and multispectral images in remote sensing applications. This method can be categorized as a ...

For instance, you could train an autoencoder on grainy images and then use the trained model to remove the grain/noise from the image. Autoencoder Architecture. Let’s take a look at the architecture ...

Electrical capacitance tomography (ECT) image reconstruction has developed decades and made great achievements, but there is still a need to find new theory framework to make image reconstruction ...

At 1 sample per pixel (spp), the Monte Carlo integration of indirect illumination results in very noisy images, and the problem can therefore be framed as reconstruction instead of denoising. Previous ...

To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed ...