According to my question in this link, I applied (with a big help from lennon310) the separability of Gabor filters of any orientation. The only disadvantage of this method is the convolution in the complex domain. So we can treat the Gabor filter G(x,y) as a two dimensional matrix that has a low rank, since the filter is fairly smooth at lower frequencies. Singular value decomposition can then be used in order to decompose the filter into a linear sum of real separable filters.

Here, ui, vi are the columns of the orthogonal matrices U,V. s_i is the singular value. The convolution of the filter G(x, y) with the image I(x, y) can now be approximated using:

Please I need a help about how can I edit my code in this link in order to apply that in MATLAB. Any help will be very appreciated.

According to my question in this link, I applied (with a big help from lennon310) the separability of Gabor filters of any orientation. The only disadvantage of this method is the convolution in the complex domain. So we can treat the Gabor filter G(x,y) as a two dimensional matrix that has a low rank, since the filter is fairly smooth at lower frequencies. Singular value decomposition can then be used in order to decompose the filter into a linear sum of real separable filters.

Here, ui, vi are the columns of the orthogonal matrices U,V. s_i is the singular value. The convolution of the filter G(x, y) with the image I(x, y) can now be approximated using:

Please I need a help about how can I edit my code in this link in order to apply that in MATLAB. Any help will be very appreciated.