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 ...

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 ...

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 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 ...

Adversarially Approximated Autoencoder for Image Generation and Manipulation Abstract: Regularized autoencoders learn the latent codes, a structure with the regularization under the distribution, ...

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 ...