(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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

The autoencoder consists of: Input layer: Image input layer of size 32x32x1. Encoding layers: Convolutional and max pooling layers to extract features. Decoding layers: Transposed convolutional layers ...