Guides
Long-form articles on how AI image processing actually works. Each guide takes a single technology and explains it in plain language, including what it is good at, where it struggles, and how to get the best results.
How AI Background Removal Works: U-2-Net Explained
A deep dive into salient object detection, the neural network architecture that powers modern background removers, and how to get clean cutouts every time.
Restoring Old Photos with GFPGAN: How AI Face Restoration Works
How a specialized generative model can reconstruct blurry, damaged, or low-resolution faces, and why it is different from ordinary upscaling.
AI Photo Colorization: How Neural Networks Color Black-and-White Photos
How deep learning models learn to guess plausible colors for grayscale images, what they can and cannot know, and how to get the best results from historical photographs.
AnimeGAN: Turning Photos into Anime-Style Art with AI
How generative adversarial networks learn the visual language of Japanese animation and apply it to real-world photographs.