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, ...

The quality of the image is very important in image processing. Image blurring is a most common issue, which caused to reduce the quality of the image. Blurred images often occur due to camera shake, ...

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 ...

First, as depicted in Figure 1, the convolutional autoencoder can generate an image with the same structure as the original image, but it is blurry than the original image, and some structures (such ...

Abstract: 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 ...

BrainSeg-AutoEncoder-for-Image-Segmentation Project Overview BrainSeg leverages autoencoder architectures for precise segmentation and detection of brain tumors in MRI scans. This project combines ...

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 ...