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Applications#

Click on your algorithm of choice to explore various applications. We collected the algorithms based on the type of training data they require!

Keywords#

  • no ground-truth: The algorithm trains without clean images.
  • single image: The algorithm can train on a single image.
  • pairs of noisy images: The algorithm requires pairs of noisy images.
  • ground-truth: The algorithm requires pairs of clean and noisy images.

Denoising noisy images without clean data#

You have noisy images and no clean images? No problem! These algorithms can help you, as they do not require any ground-truth data. You can also train on a single image of reasonable size.

Noise2Void

A self-supervised denoising algorithm based on a pixel masking scheme.

no ground-truth single image

If you have multiple noisy instances of the same structure (e.g. a noisy time-lapse), then Noise2Noise might be the right choice for you.