Question

Well am referring the following paper and trying to implement the algorithm as given in matlab

The only problem is how do i find a noisy pixel i.e Pixel with impulse noise?

X seems to be the impulse pixel in an image which i have to calculate

_

____________________________________________ 
Input – Noisy Image h 
_______________________________________________ 
Step 1: Compute X 
             for every pixel repeat steps from 2 to 7 
Step 2: Initialize w = 3 
Step 3: If X(i,j) ≠ Impulse pixel 
                 goto step 7  
Step 4: ∆i,j = { h(i1,j1) | i-(w-1)/2 ≤ i1 ≤ i+(w-1)/2,  
                                                j-(w-1)/2 ≤ j1 ≤ j+(w-1)/2} 
             b=no. of  black pixels in the window 
             w=no. of white pixels in the window 
Step 5: If ∆i,j ≠ NULL 
                    p(i,j) = mean(∆i,j
) 
                    d(i,j) = | h(i,j) – p(i,j) | 
            else   if (w < wmax) 
                           w=w+2 
                           goto step 4 
           else   
                     if (b>w) 
                           h(i,j)=0 
                    else 
                           h(i,j)=255 
Step 7: Goto next pixel 
Step 8: Calculate threshold t, from detailed coefficient  
                  matrix d 
            for every pixel 
Step 9: If (d(i,j)>t) 
                    h(i,j)=p(i,j) 
____________________________

Edit: To implement the PSM or the median filter method we
need to set some parameters and a threshold value. This
threshold value is dependent on the image and the noise
density. So, to restore different images we need to check for
a range of threshold values and find out the best one. So, in
our proposed algorithm we removed the need to define a threshold value. The algorithm is intelligent and determines
the threshold automatically.

Answer 1

From the paper it seems that the “impulse pixels” are just the noisy pixels, in the case of salt & pepper noise. Furthermore, it also seems that the algorithm provides an “intelligent” mechanism to calculate the denoised value of a noisy pixel if its value is above a threshold (which it calculates adaptively).

So, what about “If X(i,j) ≠ Impulse pixel ” ? Well, apparently, the authors assume to know (!) which pixels are noisy (!!), which makes the whole thing rather ridiculous, since this info is almost impossible to know.

I might also add that the rather stunning results presented in the paper are most probably due to this fact.

P.S. Regarding the argument that <“impulse pixels” are all the pixels a which are either equal to 0 or 255>, it is wrong. The set of pixels that have either 0 or 255 intensity value, includes the noisy pixels as well as proper pixels that just happen to have such a value. In this case, the algorithm will most probably collapse since it will denoise healthy pixels as well.