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>

packages/shared/src/i18n/en.ts

@@ -92,6 +92,10 @@ export const en = {
       name: "Red Eye Removal",
       description: "AI-powered red eye detection and correction for flash photos",
     },
+    "restore-photo": {
+      name: "Photo Restoration",
+      description: "Fix scratches, tears, and damage on old photos with AI",
+    },
     "content-aware-resize": {
       name: "Content-Aware Resize",
       description: "Intelligently resize images while preserving important content",