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feat(i18n): 21-language pipeline, landing/docs/API wiring, landing+API translations
Shared Claude Code translation pipeline (scripts/i18n, no API key) plus Astro/VitePress/Scalar i18n wiring. Landing and API reference translated into all 20 languages; docs i18n wiring + English source anchors. The translated docs markdown (apps/docs/<locale>/**, 3,620 files) follows in a companion PR because it exceeds GitHub's per-PR CI file limit.
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description: AI-powered noise and grain removal with multi-tier quality options.
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---
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# Noise Removal
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# Noise Removal {#noise-removal}
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AI-powered noise and grain removal with multi-tier quality options, using the Python sidecar (SCUNet model).
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## API Endpoint
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## API Endpoint {#api-endpoint}
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`POST /api/v1/tools/image/noise-removal`
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**Model bundle:** `upscale-enhance` (5-6 GB)
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## Parameters
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## Parameters {#parameters}
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| Parameter | Type | Required | Default | Description |
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|-----------|------|----------|---------|-------------|
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| format | string | No | `"original"` | Output format: `original`, `png`, `jpeg`, `webp`, `avif`, `jxl` |
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| quality | number | No | `90` | Output encoding quality (1-100) |
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## Example Request
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## Example Request {#example-request}
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```bash
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curl -X POST http://localhost:1349/api/v1/tools/image/noise-removal \
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-F 'settings={"tier":"quality","strength":60,"detailPreservation":70,"colorNoise":40}'
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```
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## Response
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## Response {#response}
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### Initial Response (202 Accepted)
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### Initial Response (202 Accepted) {#initial-response-202-accepted}
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```json
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{
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}
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```
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### Progress (SSE at `/api/v1/jobs/{jobId}/progress`)
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### Progress (SSE at `/api/v1/jobs/{jobId}/progress`) {#progress-sse-at-api-v1-jobs-jobid-progress}
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```
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event: progress
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data: {"phase":"processing","stage":"Denoising...","percent":65}
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```
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### Final Result (via SSE)
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### Final Result (via SSE) {#final-result-via-sse}
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```json
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{
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}
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```
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## Notes
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## Notes {#notes}
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- Requires the `upscale-enhance` model bundle to be installed (5-6 GB).
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- Quality tiers trade speed for quality: `quick` is fastest with basic denoising, `maximum` uses the most thorough multi-pass approach.
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