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/constants.ts

@@ -209,6 +209,14 @@ export const TOOLS: Tool[] = [
     icon: "Eye",
     route: "/red-eye-removal",
   },
+  {
+    id: "restore-photo",
+    name: "Photo Restoration",
+    description: "Fix scratches, tears, and damage on old photos with AI",
+    category: "ai",
+    icon: "Undo2",
+    route: "/restore-photo",
+  },
   // Watermark & Overlay
   {
     id: "watermark-text",
@@ -432,4 +440,5 @@ export const PYTHON_SIDECAR_TOOLS = [
   "enhance-faces",
   "noise-removal",
   "red-eye-removal",
+  "restore-photo",
 ] as const;