2D and 3D Perlin Noise in MATLAB

contrast_s.m

s  =  (cos(s * pi - pi) + 1) / 2;

noise3D.m

function s = noise3D (m,k)
  s = zeros([m,m,m]);
  w = m;
  i = 0;
  while w > 3
    i = i + 1;
    d = smooth3(randn([m,m,m]), 'gaussian',k,i);
    s = s + i * d(1:m, 1:m, 1:m);
    w = w - ceil(w/2 - 1);
  end
  s = (s - min(min(s(:,:)))) ./ (max(max(s(:,:))) - min(min(s(:,:))));
end

norm_s.m

s = (s - min(min(s(:,:)))) ./ (max(max(s(:,:))) - min(min(s(:,:))));

perlin.m

function s = perlin (m)
  s = zeros(m);    # output image
  w = m;           # width of current layer
  i = 0;           # iterations
  while w > 3
    i = i + 1;
    d = interp2(randn(w), i-1, "spline");
    s = s + i * d(1:m, 1:m);
    w -= ceil(w/2 - 1);
  end
end

perlin2D.m

function s = perlin2D (m)
  s = zeros([m,m]);     % Prepare output image (size: m x m)
  w = m;
  i = 0;
  while w > 3
    i = i + 1;
    d = interp2(randn([m,m]), i-1, 'spline');
    s = s + i * d(1:m, 1:m);
    w = w - ceil(w/2 - 1);
  end
  s = (s - min(min(s(:,:)))) ./ (max(max(s(:,:))) - min(min(s(:,:))));
end

perlin3D.m

function s = perlin3D (m)
  s = zeros([m,m,m]);     % Prepare output image (size: m x m x m)
  w = m;
  i = 0;
  while w > 3
    i = i + 1;
    d = interp3(randn([m,m,m]), i-1, 'spline');
    s = s + i * d(1:m, 1:m, 1:m);
    w = w - ceil(w/2 - 1);
  end
  s = (s - min(min(s(:,:)))) ./ (max(max(s(:,:))) - min(min(s(:,:))));
end

I am afraid that this is probably value noise instead of Perlin noise.