Search Results for "generative"

Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. ...

ESRGAN stands for Enhanced Super-Resolution Generative Adversarial Network and is a foundational project in the field of deep learning-based image super-resolution. It builds on earlier GAN-based approaches by improving network architecture (e.g., using Residual-in-Residual Dense Blocks), adversarial loss functions, and perceptual loss components to generate higher-fidelity high-resolution images from low-resolution inputs with more realistic textures and details.

...With this library you can feed a set of sites (points) and compute their Voronoi cells – the partition of the plane into regions closest to each site – in O(n log n) time. It’s especially useful in web UIs, visualizations, interactive maps, and generative-art contexts where you need dynamic tessellations or diagrammatic layouts. The library exposes API functions for computing cells, retrieving neighbors, and drawing results into canvas or SVG. Because it is pure JavaScript and self-contained, it integrates easily with browser or Node.js applications without heavy dependencies. For developers exploring spatial algorithms, generative UI, or interactive diagrams, this codebase is a practical reference and tool to build upon.