12
$\begingroup$

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?

$\endgroup$
13
$\begingroup$

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).

$\endgroup$
1
0
$\begingroup$

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.

$\endgroup$
1
  • $\begingroup$ 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

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.