I want to implement the following algorithm. Let $I$ denote noisy image and for each pixel of $I$ is represented by $I(x,y)$. A Sliding window of size $3\times3$ centered at $I(x, y)$ is defined. At first we estimate that the center pixel of window is noise or not. To differ the noise and signal, we calculate the mean ($m$) and standard deviation ($s$) of the window. If the pixel is between $m-s$ and $m+s$ then it is a signal otherwise noise and we will apply weiner Filter.


1.  Read an Image I.
2.  Take a Sliding Window or Mask of size 3X3.
3.  Calculate the Mean (m) and Standard deviation (s) from the Mask.
4.  Calculate the threshold as follows 
         t1 = m-s and t2 = m+s

5.  IF     t1 <= I(x,y) and  I(x,y)  <= t2 THEN

           Result(x,y) = I(x,y)
        Result(x, y) = weiner2(I(x,y)

6.  Repeat the step 3 , 4 and 5 on Entire Image.
7.  Display the Resultant Image.

First, I tried to implement this algorithm for median filter, so that I can extend it to other filters such as wiener, anisotropic diffusion,butterworth etc. I did implement it correctly with the help of 'tbirdal'. Thank you 'tbirdal'. But I am unable to extend the implementation for other filters.

**I don't want to use loop.. Because it takes more time to run .. 20 mins to run a $512 \times 512$ image.. I want to implement this using functions such as nlfilter, imfilter etc ** Program:


fun = @(x) noisedetect_2(x(:));
B = nlfilter(A,[3 3],fun);

imshow(A), figure, imshow(B, [])

The noisedetect_2 function is given as

function nd=noisedetect_2(sub)
[row col]=size(sub);

if (sub(2,2)>=t1 && sub(2,2)<=t2)

I get the following error:

Assignment has more non-singleton rhs dimensions than non-singleton subscripts

Error in nlfilter (line 75)
        b(i,j) = feval(fun,x,params{:});

Error in nd_test_2 (line 10)
B = nlfilter(A,[3 3],fun);

I understand what the error means. the nlfilter is expecting single value but the function returns $3\times3$ matrix. How to solve this issue?. I just want to apply any filter only when the pixel falls out of the range $t_1$ and $t_2$ because it is considered as noisy pixel. Otherwise, it is retained as such because it is considered as noisefree pixel.

Please help me implement the algorithm.


I would implement it differently altogether.
Since applying the Wiener filter is pretty "cheap" I would create an Image called mWienerFilter.

vNoisePixels = (abs(I -  mMeanImage) > noiseThr);
I(vNoisePixels) = mWienerFilter(vNoisePixels);

That would be the best thing to do in my opinion.

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Use blockproc with the BorderSize option (to achieve the overlapping between the blocks):

B = blockproc(A,[1 1],fun,'BorderSize',[1 1]);
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  • $\begingroup$ Is the function noisedetect_2 written correctly? I am getting error while implementing B = blockproc(A,[1 1],fun,'BorderSize',[1 1]); $\endgroup$ – Premnath D Mar 25 '14 at 17:48

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