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Your performance is never going to approach that of the library you're comparing to, unless you change the data layout.

I don't know Matlab, but I've done a small amount of work with OpenCV, so I can speculate how the reshape() function works.

Consider this 4✕2 array:

A B C D
a b c d

which can be represented like this in OpenCV (row-major order):

width=4, height=2,
content = { A, B, C, D, a, b, c, d }

To reshape to a 4✕2 array, all we do is change the width and height members, without touching the content.

But my guess is that Matlab indexes the content array in the opposite (column-major) order:

height=2, width=4
content = { A, a, B, b, C, c, D, d }

Reshaping this by swapping width and height yields

A C
a c
B D
b d

So the key to performance may well be to change order of dimensions in the translated code so that CV reshape does what we want - instead of declaring arrays as height✕width and indexing using (y, x), declare them as width✕height and index them as (x, y). Extending this from 2 dimensions to 3 shouldn't be a problem.

To avoid error-prone edits all over the code, I recommend writing a small wrapper around cv::Mat_ which can be addressed in Matlab order.