最终代码将是：

for i = -filtSizeL:filtSizeR
            for j = -filtSizeL:filtSizeR

                if ( sqrt(i^2+j^2)>filtSize/2 )
                    E = 0;
                else
                    x = i*cos(theta) - j*sin(theta);
                    y = i*sin(theta) + j*cos(theta);

                     %fx = exp(-(i^2)/(2*sigmaq))*exp(sqrt(-1)*i*cos(theta));
                     %gy = exp(-(j^2)/(2*sigmaq))*exp(sqrt(-1)*j*sin(theta));
                     %E=real(fx*gy);

                   E = exp(-(x^2+G^2*y^2)/(2*sigmaq))*cos(2*pi*x/lambda(k));
                end
                f(j+center,i+center) = E;
            end
        end

        f = f - mean(mean(f));
        f = f ./ sqrt(sum(sum(f.^2)));

%%%% SVD %%%%
        [u,s,v]=svd(f);

 convv = zeros(size(image_double));
   for i = 1:filtSizeR+filtSizeL+1
       convv1=imfilter(image_double*s(i,i),  u(i,:),'conv');
        convv2=imfilter(double(convv1),v(:,i)','conv');
        convv = convv + convv2;
    end

    figure
    imagesc(convv);
    colormap(gray);

我不建议您在 Gabor 过滤器中应用 SVD，因为它不会带来太多好处，反而会增加计算负载。如果你实现 SVD，你可能不会首先分离过滤器，而是在2D Gabor 过滤器上执行 SVD ：

                for i = -filtSizeL:filtSizeR
                    for j = -filtSizeL:filtSizeR

                        if ( sqrt(i^2+j^2)>filtSize/2 )
                            E = 0;
                        else
                            x = i*cos(theta) - j*sin(theta);
                            y = i*sin(theta) + j*cos(theta);
                            E = exp(-(x^2+G^2*y^2)/(2*sigmaq))*cos(2*pi*x/lambda(k));
                        end
                        f(j+center,i+center) = E;
                    end
                end

               %% SVD %%%%
               [u,s,v]=SVD(f);

使用u、s和v，您可以使用图像实现卷积：

    convv = zeros(size(image_double));
    for i = 1:filtSizeR+filtSizeL+1
        convv1=imfilter(image_double*s(i,i),  u(i,:),'conv');
        convv2=imfilter(double(convv1),v(:,i)','conv');
        convv = convv + convv2;
    end

    figure
    imagesc(imag(convv));

但是我认为这种方法不如您的可分离方法f和g方法。