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Topaz Image Denoise

Topaz Image Denoise

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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.

Features

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

ParameterRequiredDescription
imageYesSource image to denoise (upload or URL)
modelNoDenoise strength to use (default: Normal)
output_formatNoOutput format: jpeg, jpg, or png

Model Options

ModelDescription
NormalBalanced noise reduction for typical images (default)
StrongMore aggressive denoise for noisier images
ExtremeMaximum noise reduction for very noisy images

Output Format Options

  • jpeg / jpg — 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 — choose denoise strength based on noise level.
  3. Choose output format — select based on your quality and file size needs.
  4. Run — submit and download the denoised image.

Pricing

ItemCost
Per image$0.07

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

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/topaz/image/denoise" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "model": "Normal",
    "output_format": "jpeg",
    "enable_base64_output": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
imagestringNo-The image file to be processed. Supported formats (png jpg jpeg tiff tif)
modelstringNoNormalNormal, Strong, ExtremeThe denoise model to use. Normal: Balanced noise reduction. Strong: More aggressive noise removal. Extreme: Maximum noise reduction for heavily degraded images.
output_formatstringNojpegjpeg, jpg, pngThe format of the output image.
enable_base64_outputbooleanNofalse-If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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