sairyscan.reconstruction.ISFEDDenoising

sairyscan.reconstruction.ISFEDDenoising#

class sairyscan.reconstruction.ISFEDDenoising(epsilon: float = 0.3, reg_inner: float = 0.995, reg_outer: float = 0.995, weighting_inner: float = 0.9, weighting_outer: float = 0.9, join_denoising: bool = False)#

Reconstruct a high resolution image with a ISFED method including a denoising step

The denoising is performed on the two terms of the ISFED image difference using the SPITFIR(e) algorithm

Parameters:

  • epsilon – weighting parameter for the ISFED difference second term. If epsilon=’map’, epsilon is an automatic estimated weight map using the SURE criterion. If epsilon=’mode’, epsilon is a float which corresponds to the main mode of the SURE map. Otherwise, epsilon can be fixed to any float value

  • reg_inner – Regularization for denoising of the first term of ISFED reconstruction

  • reg_outer – Regularization for the denoising of the second term of the SFED reconstruction

  • weighting_inner – Weighting parameter of the SPITFIR(e) denoising model for the first term of ISFED. Must be in [0, 1], with value close to 0 for sparse signal and close to one otherwise.

  • weighting_outer – Weighting parameter of the SPITFIR(e) denoising model for the second term of ISFED. Must be in [0, 1], with value close to 0 for sparse signal and close to one otherwise.

__init__(epsilon: float = 0.3, reg_inner: float = 0.995, reg_outer: float = 0.995, weighting_inner: float = 0.9, weighting_outer: float = 0.9, join_denoising: bool = False)#

Methods

__init__([epsilon, reg_inner, reg_outer, ...])

add_observer(observer)

Add an observer

notify(message)

Notify progress to observers

progress(value)

Notify progress to observers
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