Topaz Image Denoise API

Topaz Image Denoise

Topaz Image Denoise is a professional-grade noise reduction model powered by Topaz Labs' AI technology. Upload your image and select from multiple denoise strengths — AI intelligently removes noise while preserving detail and texture.

Why Choose This?

AI-powered noise reduction Intelligently removes noise while preserving important image details and textures.
Multiple strength levels Choose from Normal, Strong, or Extreme based on your noise level and quality needs.
Detail preservation Unlike traditional denoise tools, AI preserves edges, textures, and fine details.
Professional quality Powered by Topaz Labs' AI, trusted by professional photographers worldwide.

Parameters

Parameter	Required	Description
image	Yes	Source image to denoise (upload or URL)
model	No	Denoise strength to use (default: Normal)
output_format	No	Output format: jpeg, jpg, or png

Model Options

Model	Description
Normal	Balanced noise reduction for typical images (default)
Strong	More aggressive denoise for noisier images
Extreme	Maximum noise reduction for very noisy images

Output Format Options

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

How to Use

Upload your image — drag and drop or paste a URL.
Select model — choose denoise strength based on noise level.
Choose output format — select based on your quality and file size needs.
Run — submit and download the denoised image.

Pricing

Item	Cost
Per image	$0.15

Simple flat-rate pricing regardless of image size or model selected.

Best Use Cases

High ISO Photos — Clean up grainy images shot at high ISO settings.
Low Light Photography — Remove noise from night and indoor photos.
Video Stills — Clean up frames extracted from video footage.
Scanned Photos — Reduce grain and noise from scanned film images.
Smartphone Photos — Improve quality of noisy mobile phone images.

Pro Tips

Start with Normal for most images — only use Strong or Extreme if needed.
Over-denoising can make images look plastic — find the right balance.
Works best when combined with sharpening after denoise.
For archival quality, export as PNG to avoid additional compression artifacts.

Notes

Higher strength settings remove more noise but may reduce fine detail.
Best results come from images with uniform noise patterns.
For images with both noise and blur, denoise first, then sharpen.

Related Models

Topaz Image Sharpen — AI-powered image sharpening.
Topaz Image Lighting — AI-powered lighting adjustment.
Topaz Image Restore — AI-powered dust and scratch removal.

Image Denoise API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/topaz/image/denoise with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Image Denoise below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/topaz/image/denoise" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "image": "https://example.com/your-input.jpg",
    "model": "Normal",
    "output_format": "jpeg",
    "enable_base64_output": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("topaz/image/denoise", {
        "image": "https://example.com/your-input.jpg",
        "model": "Normal",
        "output_format": "jpeg",
        "enable_base64_output": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "topaz/image/denoise",
    {
    "image": "https://example.com/your-input.jpg",
    "model": "Normal",
    "output_format": "jpeg",
    "enable_base64_output": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Image Denoise API — Frequently asked questions

What is the Image Denoise API?

Image Denoise is a Topaz model for image editing, exposed as a REST API on WaveSpeedAI. Topaz Image Denoise removes grain and high-ISO noise while preserving detail. Perfect for low-light photography and high-ISO images. 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 Denoise 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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/topaz/topaz-image-denoise.

How much does Image Denoise cost per run?

Image Denoise starts at $0.15 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 Denoise 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-denoise.

How long does Image Denoise take to generate?

Average end-to-end generation time on WaveSpeedAI is around 13 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Image Denoise 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.

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