Class: NodeImageLandweberDeconvolution

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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.



Input image.

Type: Image4DFloat, Required, Single


Kernel image.

Type: Image4DFloat, Required, Single



Resulting image.

Type: Image4DFloat


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.


  1. “The Insight Segmentation and Registration Toolkit”