Help increasing the speed of a piece of code

 Dear all,

 

Plain summary: I have a temperature matrix and I need to find the average temperature of the 8 pixels surrounding each pixel.

 

I need to subdivide my 134x134 matrices into 3x3 sub matrices. For each submatrix I need to compute the mean and the standard deviation, excluding the central pixel from the calculation.

 

Then I need to find the values exceding the upper and lower boundaries, namely the mean + standard deviation and the mean - standard deviation, respectively. Then, I need to recalculate the mean excluding the values falling outside the lower and upper boundaries and assign the mean value to the central pixel location (in a new matrix).

 

The output will be a 134x134 matrix with every value (pixel) corresponding to the average of its surrounding pixels with the "outliers" excluded from the average.

 

A=magic(134); % Assuming this is my 134x134 Temperature matrix

T_background = blockproc(A,[1,1],@BlkFun, 'BorderSize',[1,1],...
    'TrimBorder',false, 'PadMethod',NaN);

%where BlkFun=

function v = BlkFun(s)
px = s.data;
if isequal(size(px),[3,3])
    px(5) = [];
    av = nanmean(px);
    sd = nanstd(px);
    ix = px>=(av-sd) & px<=(av+sd);
    v = nanmean(px(ix));
else
    v = NaN;
end
end

 

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It sounds like a convolution would be perfect here.

 

kernel=ones(3,3);
kernel(2,2)=0;
kernel=kernel/sum(kernel(:));

That will find the average of the surrounding pixels.

 

Another strategy would be to create a 3D array with all shifts. Then you can use the functions already in your function. Instead of removing elements (or indexing) you should mark invalid values with NaN. 

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