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Change loop to matrix operation in matlab (image processing)

Posted on 2008-10-29
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Last Modified: 2016-02-10
Hei!

I have a problem with image processing in matlab for a lab course. I have only basic matlab knowledge and hope to be able to explain my problem with my basic school english...
The code attached is a loop in matlab to calculate a "moving average" on a picture. The first and second loop is for the rows (german: Zeilen) and columns (Spalten) of the picture (P), the third and fourth loop is for the rows and columns of the filter matrix (kern). Then it sums up the values of the filter matrix and divides it by the sum of the loadings. ewr and ewc are needed to calculate the new border of the image. (zero padding or replicate)

My question:
I need a way to do this with fast matrix/vector operations if possible.
I am not allowed to use any imfilter, conv,  conv2, block-processing or sliding-window functions.
What i can imagine is to exchange the 2 inner loops by a matrix-multiplication. I would multiply the filter matrix with the right submatrix of the image. But to replace the outer loops i need to know the position/index to know wich submatrix i have to multiply...
I read something about the not so well known accumarray() function but i don't know how i can use this for 2D operation.
Maybe someone can give me a hint...

Thanks in advance!

Greetings from Vienna, Austria.

beta
for ip = (ewr+1):(rc(1)+(ewr)) %Zeilen

    for np = (ewc+1):(rc(2)+ewc)%Spalten

        for Z = -ewr:ewr

            for S = -ewc:ewc

            PF(ip,np)=PF(ip,np)+P(ip+Z,np+S)*kern(Z+ewr+1,S+ewc+1);

            end

        end

        PF(ip,np)=PF(ip,np)/(sum(sum(kern,1),2));  

    end

end

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Question by:strupet
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i did it with a matrix multiplication (see attached code) but it seems much slower than the previous version with four loops.
for ip = (row_ext+1):(rc(1)+row_ext) %rows

    for np = (col_ext+1):(rc(2)+col_ext)%columns

        PF(ip,np)=sum(sum(kern.*P( (ip-row_ext):(ip+row_ext) , (np-col_ext):(np+col_ext)),1),2)/sum(sum(kern,1),2);

    end

end

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