Model overview¶
This is an overview of the currently supported restoration models that are tailored to commonly-used imaging scenarios:
csbdeep.models.CARE¶
- Description:
Standard model that learns a mapping from source (degraded) to target (restored) images.
Source/target can be (multi-channel) 2D or 3D stacks.
Expects spatially registered source/target pairs.
- Typical use-case:
Denoising of live-cell images (e.g. acquired with reduced laser power/exposure).
Improving SNR of fast time-lapses (e.g. of vesicle trafficking).
- Examples:
csbdeep.models.UpsamplingCARE¶
- Description:
Extension of the standard model that will additionally increase sampling along a given (e.g. axial) dimension by a given factor
s.Source/target pairs should be registered 3D stacks with the desired pixel size.
After training, the model is applied to lower-resolution data producing target stacks with an
s-fold increased number of sample planes.- Typical use-case:
Improving the axial resolution of volumetric time-lapses when only a limited number of focal planes can be acquired.
- Examples:
csbdeep.models.IsotropicCARE¶
- Description:
Model that improves axial resolution of (axially) anisotropic stacks.
Takes anisotropic 3D stacks as source (important: doesn’t need corresponding target stacks).
The PSF of the microscope has to be (approximately) known.
Assumes isotropic distribution of biological structures (i.e. don’t use it with highly anisotropic tissue like cortical tissue).
- Typical use-case:
Enhancing axial resolution of (already acquired) light-sheet microscopy time-lapses of developing embryos.
- Examples:
csbdeep.models.ProjectionCARE¶
- Description:
Extension of the standard model that will additionally project along a given (default: axial) dimension.
While source images should be 3D stacks, the corresponding target images are missing an axis and thus are 2D images.
- Typical use-case:
Projection and denoising of a 2D manifold (e.g. epithelial tissue) inside a 3D stack.
- Examples: