George Perdrizet

Model

compression_autoencoder

George Perdrizet

A convolutional autoencoder trained to compress 256×256 RGB images into a compact 1024-dimensional latent representation, achieving 192× compression ratio. This model learns to compress high-quality images by encoding them into a compact latent space, then reconstructing them with minimal quality loss. The encoder reduces a 196,608-value image (256×256×3) to just 1024 numbers, while the decoder reconstructs the original image from this compressed representation. This model is designed for educational purposes to demonstrate how autoencoders can learn compression automatically from data, rather than using hand-crafted rules like JPEG or PNG. Trained on DF2KOST, a combined dataset of 26.8k…

Model

denoising_autoencoder

George Perdrizet

A convolutional autoencoder trained to remove Gaussian noise from 256×256 RGB images. The model learns the manifold of clean images and projects noisy inputs back onto this learned space. This model removes Gaussian noise from corrupted images by learning to map noisy images back to their clean versions. During training, the autoencoder learns the distribution of clean images, enabling it to filter out noise while preserving image content. - σ=75 (on 0-255 scale) Gaussian noise - Approximately 0.294 on [0,1] normalized scale This model is designed for educational purposes to demonstrate how autoencoders can learn to denoise images by understanding the structure of clean data. - Exploring…