RainLab is a zero-dependency browser playground for rain synthesis, lightweight deraining previews, and shareable before/after cards.
Image source: public GitHub Pages demo screenshot, https://starymoon.github.io/RainLab/. Captured on 2026-07-02.
It is designed for computer vision researchers, students, and builders who want to explain weather degradation quickly without installing Python environments, downloading model weights, or uploading private images to a server.
- Runs entirely in the browser.
- Upload any local image, synthesize controllable rain, and preview a lightweight deraining heuristic.
- Export a polished before/after PNG for README files, slides, and social posts.
- Uses no dependencies, no backend, and no tracking.
- Small enough to read in one sitting.
- Controllable rain amount, streak length, wind angle, haze, and derain strength.
- Split, rainy-only, and cleaned-only preview modes.
- Generated sample scene, so the demo works immediately.
- Drag-and-drop local image loading.
- Synthetic clarity, rain pixel, contrast, seed, and runtime readouts.
- One-click recipe copy for reproducible screenshots.
Open index.html directly, or run a tiny local server:
python3 -m http.server 8080Then visit:
http://localhost:8080
This project is static. Enable GitHub Pages from the repository settings and
serve from the main branch root.
RainLab is not a neural deraining model. It is a visual sandbox for:
- teaching image degradation,
- generating paper-demo cards,
- building intuition for deraining datasets,
- explaining why weather-robust perception is hard.
For real deraining research, see Minghao Liu's work on continual rain removal and prompt-based image restoration:
I built RainLab: a tiny browser playground for image deraining demos.
Upload an image, synthesize controllable rain, preview a lightweight derain heuristic, and export a before/after card. No backend, no dependencies, no model weights. Everything runs locally.
Repo: https://github.com/StaryMoon/RainLab
MIT