Denoise Restoration
Classic
- Procedural API:
- aydin.classic_denoise(noisy, *, batch_axes=None, chan_axes=None, variant=None)[source]
Method to denoise an image with Classic denoising restoration module.
- Parameters
- noisynumpy.ndarray
Image to denoise
- batch_axesarray_like, optional
Indices of batch axes.
- chan_axesarray_like, optional
Indices of channel axes.
- variantstr
Algorithm variant.
- Returns
- Denoised imagenumpy.ndarray
- Object-Oriented API:
- class aydin.restoration.denoise.classic.Classic(*, variant: Optional[str] = None, use_model=None, input_model_path=None, lower_level_args=None, it_transforms=None)[source]
Classic Image Denoising
- static archive(source, destination)
Archives the model to given destination.
- Parameters
- sourcestr
- destinationstr
- static clean_model_folder(model_folder)
Method to clean model folder created
- property configurable_arguments
Returns the configurable arguments that will be exposed on GUI and CLI.
- denoise(noisy_image, *, batch_axes=None, chan_axes=None, **kwargs)[source]
Method to denoise an image with trained Noise2Self FGR.
- Parameters
- batch_axesarray_like, optional
Indices of batch axes.
- chan_axesarray_like, optional
Indices of channel axes.
- noisy_imagenumpy.ndarray
- Returns
- responsenumpy.ndarray
- get_translator()[source]
Returns the corresponding translator instance for given selections.
- Parameters
- feature_generatorFeatureGeneratorBase
- regressorRegressorBase
- Returns
- itImageTranslatorBase
- property implementations
Returns the list of discovered implementations for given method.
- load(model_path: str)
- Parameters
- model_pathstr
whole path to the model including the model zip name
- save(model_path)
Saves the latest trained model next to the input image file.
- Parameters
- model_pathstr
Noise2SelfFGR
- Procedural API:
- aydin.noise2self_fgr(noisy, *, batch_axes=None, chan_axes=None, variant=None)[source]
Method to denoise an image with trained Noise2Self FGR.
- Parameters
- noisynumpy.ndarray
Image to denoise
- batch_axesarray_like, optional
Indices of batch axes.
- chan_axesarray_like, optional
Indices of channel axes.
- variantstr
Algorithm variant.
- Returns
- Denoised imagenumpy.ndarray
- Object-Oriented API:
- class aydin.restoration.denoise.noise2selffgr.Noise2SelfFGR(*, variant: Optional[str] = None, use_model=None, input_model_path=None, lower_level_args=None, it_transforms=None)[source]
Noise2Self image denoising using the “Feature Generation & Regression” ( FGR) approach. Follows from the theory exposed in the <a href=”https://arxiv.org/abs/1901.11365”>Noise2Self paper</a>.
- static archive(source, destination)
Archives the model to given destination.
- Parameters
- sourcestr
- destinationstr
- static clean_model_folder(model_folder)
Method to clean model folder created
- property configurable_arguments
Returns the configurable arguments that will be exposed on GUI and CLI.
- denoise(noisy_image, *, batch_axes=None, chan_axes=None, **kwargs)[source]
Method to denoise an image with trained Noise2Self FGR.
- Parameters
- batch_axesarray_like, optional
Indices of batch axes.
- chan_axesarray_like, optional
Indices of channel axes.
- noisy_imagenumpy.ndarray
- Returns
- responsenumpy.ndarray
- get_generator()[source]
Returns the corresponding generator instance for given selections.
- Returns
- generatorFeatureGeneratorBase
- get_regressor()[source]
Returns the corresponding regressor instance for given selections.
- Returns
- regressorRegressorBase
- get_translator(feature_generator, regressor)[source]
Returns the corresponding translator instance for given selections.
- Parameters
- feature_generatorFeatureGeneratorBase
- regressorRegressorBase
- Returns
- itImageTranslatorBase
- property implementations
Returns the list of discovered implementations for given method.
- load(model_path: str)
- Parameters
- model_pathstr
whole path to the model including the model zip name
- save(model_path)
Saves the latest trained model next to the input image file.
- Parameters
- model_pathstr
Noise2SelfCNN
- Procedural API:
- aydin.noise2self_cnn(image, *, batch_axes=None, chan_axes=None, variant=None)[source]
Method to denoise an image with Noise2Self CNN.
- Parameters
- imagenumpy.ndarray
Image to denoise
- batch_axesarray_like, optional
Indices of batch axes.
- chan_axesarray_like, optional
Indices of channel axes.
- variantstr
Algorithm variant.
- Returns
- Denoised imagenumpy.ndarray
- Object-Oriented API:
- class aydin.restoration.denoise.noise2selfcnn.Noise2SelfCNN(*, variant: Optional[str] = None, use_model=None, input_model_path=None, lower_level_args=None, it_transforms=None)[source]
Noise2Self image denoising using the “Convolutional Neural Networks” ( CNN) approach. Follows from the theory exposed in the <a href=”https://arxiv.org/abs/1901.11365”>Noise2Self paper</a>.
- static archive(source, destination)
Archives the model to given destination.
- Parameters
- sourcestr
- destinationstr
- static clean_model_folder(model_folder)
Method to clean model folder created
- property configurable_arguments
Returns the configurable arguments that will be exposed on GUI and CLI.
- denoise(noisy_image, *, batch_axes=None, chan_axes=None, **kwargs)[source]
Method to denoise an image with trained Noise2Self.
- Parameters
- batch_axesarray_like, optional
Indices of batch axes.
- chan_axesarray_like, optional
Indices of channel axes.
- noisy_imagenumpy.ndarray
- Returns
- responsenumpy.ndarray
- get_translator()[source]
Returns the corresponding translator instance for given selections.
- Returns
- itImageTranslatorBase
- property implementations
Returns the list of discovered implementations for given method.
- load(model_path: str)
- Parameters
- model_pathstr
whole path to the model including the model zip name
- save(model_path)
Saves the latest trained model next to the input image file.
- Parameters
- model_pathstr