Uncovering the Blurred Image: Finding K & f

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Homework Help Overview

The discussion revolves around an inverse problem related to image processing, specifically focusing on the identification of parameters K and f in the equation A=Ku+f, where A represents a blurred image, u is the original image source, K denotes the transformation applied (such as blurring), and f accounts for noise. The original poster seeks to understand how to determine K and f, particularly in the context of Gaussian blurring and the imnoise function in MATLAB.

Discussion Character

  • Exploratory, Assumption checking

Approaches and Questions Raised

  • Participants discuss the nature of the problem as an inverse problem and suggest the use of regularization techniques. The original poster expresses a desire for clarity on how to find the specific parameters K and f.

Discussion Status

The conversation includes references to theoretical resources on inverse problems and regularization methods. While some guidance has been offered regarding the use of regularization, there is no explicit consensus on the specific methods to apply or the details of the parameters sought.

Contextual Notes

The original poster indicates a specific interest in replicating effects seen in MATLAB, which may imply constraints related to software or specific methodologies in image processing. There is also mention of matrix representations and the challenges posed by the nature of the problem.

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Homework Statement


I found that the blurred image is always presented by
$$A=Ku+f$$, where u is the perfect image source, and the K is transformation (blurring, sampling)
and f is the noising . The question I want to know is how can we find such K, or f when we blurred the image , for example ,using the gaussian blurring.
In other word, I want to know how can I find K or f s.t. I can do the same effect as using the
imnoise function in MATLAB as gaussian blurring or ...


Homework Equations


I found that there are matrix related to it, where the matrix is given by h=fspecial('gaussian',256);
and it is all zeros.


The Attempt at a Solution

 
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It's an inverse problem ... you need to use some regularization approach.
http://home.comcast.net/~szemengtan/InverseProblems/chap3.pdf
 
Last edited by a moderator:
um,, I just want to know how can I find such K and f...
Thanks you.:)
 
And I told you - use a regularization technique.
I'm not going to write out the 10-20 pages of theory here - hence the link.
 

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