poster
2019-11-25, 05:20
<p>I am un familiar with matlab. I have calculated a kernel of size NxN refered to as mask in the code below. I then pad the image, before applying the convolution operation seen below. </p>
<pre><code>for i=1:size(temp_image,1) %temp size of output image
for j=1:size(output_image,2) %size of our output image
temp = double(padded_input(i:n-1+i,j:n-1+j)).*mask; %padded_input is our padded image
temp_image(i,j) = sum (temp, 'all');
end
end
</code></pre>
<p>This convolution calculation adds significant time to the operation, notably 3 seconds for my current test case. How can I reduce this convolution operations processing time? I would hypothesis there is a more efficient way to do this calculation.</p>
<p>Thank you</p>
More answer... (https://stackoverflow.com/questions/59022413/speeding-up-matlab-gaussian-filter-implementation)
<pre><code>for i=1:size(temp_image,1) %temp size of output image
for j=1:size(output_image,2) %size of our output image
temp = double(padded_input(i:n-1+i,j:n-1+j)).*mask; %padded_input is our padded image
temp_image(i,j) = sum (temp, 'all');
end
end
</code></pre>
<p>This convolution calculation adds significant time to the operation, notably 3 seconds for my current test case. How can I reduce this convolution operations processing time? I would hypothesis there is a more efficient way to do this calculation.</p>
<p>Thank you</p>
More answer... (https://stackoverflow.com/questions/59022413/speeding-up-matlab-gaussian-filter-implementation)