Deconvolution
Class: NodeImageLandweberDeconvolution
Deprecated!
Deconvolve an image using the Landweber deconvolution algorithm as defined in Bertero M and Boccacci P, “Introduction to Inverse Problems in Imaging”, 1998. The algorithm assumes that the input image has been formed by a linear shift-invariant system with a known kernel and is best suited for images that have zero-mean Gaussian white noise.
This is the base implementation of the Landweber algorithm. It may produce results with negative values. For a version of this algorithm that enforces a positivity constraint on each intermediate solution, use Projected Landweber Deconvolution.
Inputs
Image
Input image.
Type: Image4DFloat, Required, Single
Kernel
Kernel image.
Type: Image4DFloat, Required, Single
Outputs
Output
Resulting image.
Type: Image4DFloat
Settings
Boundary Condition Selection
Sets the method to use when calculating voxels close to the bounds of the image.
Values: ZeroPad, ZeroFluxNeumannPad, PeriodicPad
Output Region Mode Selection
Sets the output region mode.
Values: Same, Valid
Alpha Number
Relaxation factor.
Normalize Boolean
Normalize the output image by the sum of the kernel components.
Iterations Integer
Set the number of iterations.
References
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