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, ...

Image Denoising with PCA (4.1) Utilized Principal Component Analysis (PCA) as an image denoiser. Conducted subjective measures by visualizing denoised images from both autoencoder and PCA. Conducted ...

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 ...

Document digitization has an important role in helping the company’s activities be more efficient, such as detecting text in invoice document images using optical character recognition (OCR). However, ...

For picture denoising in this investigation, a CNN autoencoder with convolutional, max-pooling, and upsampling layers was used. The design consists of a decoder that reconstructs the image, an encoder ...

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 ...