Question

I’m working on image processing with CUDA and i’ve a doubt about pixel processing.

What is often done with the boundary pixels of an image when applying a m x m convolution filter?

In a 3 x 3 convolution kernel, ignoring the 1 pixel boundary of the image is easier to deal with, especially when the code is improved with shared memory. Indeed, in this case, one does not need to check if a given pixel has all the neigbourhood available (i.e. pixel at coord (0, 0) has not left, left-upper, upper neighbours). However, removing the 1 pixel boundary of the original image could generate partial results.

Opposite to that, I’d like to process all the pixels within the image, also when using shared memory improvements, i.e., for example, loading 16 x 16 pixels, but computing the inner 14 x 14. Also in this case, ignoring the boundary pixels generates a clearer code.

What is usually done in this case?

Does anyone usually use my approach ignoring the boundary pixels?

Of course, I’m aware the answer depends on the type of problem, i.e. adding two images pixel-wise has not this problem.

Thanks in advance.

Answer 1

A common approach to dealing with border effects is to pad the original image with extra rows & columns based on your filter size. Some common choices for the padded values are:

• A constant (e.g. zero)
• Replicate the first and last row / column as many times as needed
• Reflect the image at the borders (e.g. column[-1] = column[1], column[-2] = column[2])
• Wrap the image values (e.g. column[-1] = column[width-1], column[-2] = column[width-2])