3 people 6a43cc1b77 feat: SOTA AI photo restoration with multi-step pipeline (#58) (#62) ...

Add comprehensive photo restoration tool that chains multiple AI models:
- Scratch/tear/spot detection via morphological analysis (top-hat/black-hat transforms)
- Damage inpainting via LaMa ONNX model (reuses existing infrastructure)
- Face enhancement via CodeFormer ONNX (~377MB, from facefusion/models-3.0.0)
- Noise reduction via OpenCV NLMeans in LAB color space
- Optional B&W auto-colorization via DDColor (reuses existing model)

Settings: 3 restoration modes (Light/Auto/Heavy), individual feature toggles
for scratch removal, face enhancement (with fidelity slider), denoising
(with strength slider), and auto-colorize. Before/after comparison view.

Handles HEIC, HEIF, and all standard formats. Batch processing supported.
No new Python dependencies - reuses onnxruntime, cv2, mediapipe, PIL.

Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>

2026-04-13 21:57:51 +08:00

..

api

feat: SOTA AI photo restoration with multi-step pipeline (#58) (#62)

2026-04-13 21:57:51 +08:00

docs

docs(api): achieve 100% endpoint coverage in OpenAPI spec and VitePress docs (#54)

2026-04-13 17:30:08 +08:00

web

feat: SOTA AI photo restoration with multi-step pipeline (#58) (#62)

2026-04-13 21:57:51 +08:00