This demo is for research & educational use only — not a medical device. Read full disclaimer.
Research Demo Gaussian / Poisson / Mixed Natural + Medical Images

AI-Based Image Denoising

Supported Noise

Designed for Gaussian, Poisson, speckle, and mixed noise patterns.

Supported Images

Applicable to general research images and examples such as OCT, OCTA, X-ray, and MRI.

Output

Produces a denoised image while preserving important structures for research-oriented evaluation.

This page applies AI-based denoising to reduce common image noise while preserving important image features. Images with extreme blur or heavy artifacts may not be restored accurately. Please upload only images that you are permitted to use.

Images larger than 1024 pixels may be resized while preserving aspect ratio.
Upload image and run denoising
Denoising strength
Start with Level 1. For more difficult images, increase the level and optionally enable image improvement.
Level: 1
Higher denoising levels and image improvement may help with complex noise patterns, but processing time can increase!
0.0s

How to use this page

The examples below illustrate the type of input image that can be denoised and the kind of output produced by the model.This image is a synthetic denoising stress-test pattern designed to challenge multiple aspects of image restoration at once. It uses mixed synthetic corruption composed of three noise types: additive Gaussian noise, multiplicative speckle-like noise, and salt-and-pepper impulse noise. Stress test is made a little harder by first applying an additional Gaussian blur with radius 1.2 before the mixed noise is added.

Example Input
Noisy image example with complex noise

Upload a noisy image for AI-based denoising.

  • Noise may be Gaussian, Poisson, speckle, or mixed
  • Suitable for a broad range of image types
  • Can be used for research-oriented medical image examples
Example Output
Example denoised output image

The output visualization provides:

  • A denoised image generated by the AI model
  • Stronger denoising can be selected using Levels 1 to 5
  • Complex images may benefit from higher strength

This demonstration is intended for research and educational use only.