I am studying some deconvolution techniques, In order to remove motion blur, like:

Are there any pros / cons of using one versus another?
For example which are the pros / cons of Richardson-Lucy technique?


Both are the MMSE estimators.

The main difference is Wiener is the optimal for Gaussian Noise while Richardson Lucy assumes Poisson Noise.

Poisson Noise is a better model for noise in photos captured by a Photo Diode.

Computationally, in the case of Gaussian Noise and Linear Convolution the solution has a closed form solution in the Maximum Likelihood / LS sense.

The Lucy Richardson method, which is a Maximum Likelihood in the Poisson case, has no closed form solution and requires iterative approach.

See Noise, Image Reconstruction with Noise (EE367/CS448I: Computational Imaging and Display, Class 10, Gordon Wetzstein, Stanford University).


The efficiency of these approaches depends on your image, and Rechardson-Lucy has two forms both for Gaussian and Poisson noise. Rechardson-Lucy is an iterative method which can also correct spherical aberration.

  • $\begingroup$ The paper you linked to is taking the hard path to do what I did here: dsp.stackexchange.com/questions/11208. For Gaussian Noise there is no need for iterative solution. There is a closed form solution. Fro large problems we solve the linear problem using iterative methods. But not Gradient Descent as the paper derives. Welcome to our community, $\endgroup$
    – Royi
    Aug 15 at 14:38

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