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>

apps/web/src/lib/tool-registry.tsx

@@ -264,6 +264,11 @@ const RedEyeRemovalSettings = lazy(() =>
     default: m.RedEyeRemovalSettings,
   })),
 );
+const RestorePhotoSettings = lazy(() =>
+  import("@/components/tools/restore-photo-settings").then((m) => ({
+    default: m.RestorePhotoSettings,
+  })),
+);
 
 // ── Color tool wrapper ─────────────────────────────────────────────
 // Color tools share a single component but differ by toolId.
@@ -396,6 +401,7 @@ export const toolRegistry = new Map<string, ToolRegistryEntry>([
   ["colorize", { displayMode: "before-after", Settings: ColorizeSettings }],
   ["noise-removal", { displayMode: "before-after", Settings: NoiseRemovalSettings }],
   ["red-eye-removal", { displayMode: "before-after", Settings: RedEyeRemovalSettings }],
+  ["restore-photo", { displayMode: "before-after", Settings: RestorePhotoSettings }],
 ]);
 
 export function getToolRegistryEntry(toolId: string): ToolRegistryEntry | undefined {