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Topaz Image Restore enhances older and poorer quality photos through restoration. Remove dust, scratches, and damage from vintage photos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
Input
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Idle

$0.096per run·~10 / $1

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README

Topaz Image Restore

Topaz Image Restore is a professional-grade image restoration model powered by Topaz Labs' AI technology. Upload your image and let AI automatically detect and remove dust, scratches, and other imperfections — perfect for restoring old photos and scanned images.

Why Choose This?

  • Dust and scratch removal AI automatically detects and removes dust particles, scratches, and other surface imperfections.

  • Old photo restoration Ideal for restoring scanned photos, film negatives, and vintage images.

  • Professional quality Powered by Topaz Labs' AI, trusted by professional photographers and archivists.

  • Multiple output formats Export as JPEG or PNG based on your workflow needs.

Parameters

ParameterRequiredDescription
imageYesSource image to restore (upload or URL)
modelNoRestoration model to use (default: Dust-Scratch V2)
output_formatNoOutput format: jpeg or png

Model Options

ModelDescription
Dust-Scratch V2Improved detection and removal (default)

Output Format Options

  • jpeg — Compressed format, smaller file size
  • png — Lossless format, supports transparency

How to Use

  1. Upload your image — drag and drop or paste a URL.
  2. Select model — use Dust-Scratch V2.
  3. Choose output format — select based on your quality and file size needs.
  4. Run — submit and download the restored image.

Pricing

ItemCost
Per 24 input megapixels (rounded up)$0.096

The minimum charge is $0.096. Each additional started 24-megapixel block adds $0.096.

Best Use Cases

  • Old Photo Restoration — Remove dust and scratches from vintage photographs.
  • Film Scanning — Clean up scanned film negatives and slides.
  • Archive Digitization — Restore historical images for digital archives.
  • Family Photos — Bring old family photos back to life.
  • Print Restoration — Clean up scanned prints with surface damage.

Pro Tips

  • Use Dust-Scratch V2 for better results on heavily damaged images.
  • For archival work, export as PNG to avoid additional compression artifacts.
  • Combine with other Topaz tools (Sharpen, Upscale) for complete restoration workflow.
  • Best results come from high-resolution scans of the original photos.

Notes

  • This model is optimized for dust and scratch removal, not for colorization or major damage repair.
  • For best results, scan original photos at high resolution before processing.
  • V2 model generally produces better results but may take slightly longer.

Related Models

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Image Restore API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/topaz/image/restore with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Image Restore below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "model": "Dust-Scratch V2",
    "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/topaz/image/restore" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/topaz/image/restore";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "model": "Dust-Scratch V2",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "model": "Dust-Scratch V2",
    "output_format": "jpeg"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/topaz/image/restore", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

Image Restore API — Frequently asked questions

What is the Image Restore API?

Image Restore is a Topaz model for image editing, exposed as a REST API on WaveSpeedAI. Topaz Image Restore enhances older and poorer quality photos through restoration. Remove dust, scratches, and damage from vintage photos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Image Restore API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/topaz/topaz-image-restore.

How much does Image Restore cost per run?

Image Restore starts at $0.096 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Image Restore accept?

Key inputs: `image`, `enable_base64_output`, `model`, `output_format`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/topaz/topaz-image-restore.

How long does Image Restore take to generate?

Median end-to-end generation time on WaveSpeedAI is around 44 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Image Restore outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Topaz). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Image Restore | Fast Image Editing API on WaveSpeedAI