MATLAB: Does ‘imresize’ generate artificial pixel RGB values which are not contained in the original image

imresize

Hi,

I would like to know whether 'imresize' generate artificial pixel values which are not contained in the original image.

For instance, if my image is 2 rows by 3 columns (extreme case, won't happen in the real life). There will be 6 pixels contained in the image.

For instance, if these 6 pixels are

[0 0 0], [1 1 1], [2 2 2], [3 3 3], [4 4 4], [5 5 5], [6 6 6]

After I imresize the image to 1/2. There will only be 3 pixels left. Will some of these 3 pixels be like [1.5 1.5 1.5] which does not belong to the original 6 pixels.

Best Answer

Hi,

Yes for interpolation methods like bilinear, bicubic. But if you want to avoid it.

As suggested by Adam you can try interp2.

I would also suggest something similar. The imresize has 'nearest' as it's Interpolation method. You can try that too.

    J = imresize(I, 0.5, 'nearest');

This shrinks by factor of two using nearest-neighbor interpolation. This is the fastest method, but it has the lowest quality.

Related Solutions

MATLAB: How we can amplify an image with nearest neighbour interpolation

Amplify?? you mean resize? if that's what you mean here is the code

IResized= imresize(I,scale,'nearest');

MATLAB: [interp2] Different interpolation methods in each dimension.

Sorry, but while this could be faster IF you wrote it from scratch, it will probably not be so in practice, IF you tried to do it by some means. Anyway, you cannot tell interp2 to use different orders of interpolant in each dimension.

That does not mean it is impossible. It is in fact doable using spapi to create the interpolant.

x = -2:.5:2;
y=-1:.5:1;
[xx, yy] = ndgrid(x,y);
z = sin(xx+yy);
sp = spapi({2,4},{x,y},z);
fnplt(sp)
view(4,11)

That interpolant was linear in x, and cubic in y.

HOWEVER, note what appear to be scallops in the surface. These are indeed present in the interpolated surface, and are an artifact of interpolation. Note that those scallop artifacts are in fact considerably more obvious when a fully bilinear interpolant is applied to the same problem. This happens because a bilinear interpolant is not in fact quite as linear as you might think.

So, is this interpolant faster than interp2? Not so.

timeit(@() interp2(x,y,z',.15,.25,'spline'))
ans =
   0.00049108
timeit(@() fnval(sp,[.15;.25]))
ans =
    0.0013427

So for the spapi based spline interpolant that was linear in x and cubic in y, spapi was actually nearly 3 times slower than the full 2-d spline interp2 call.

Sorry. If you want significantly faster code, you would need to write it from scratch, and NOT in MATLAB.

Best Answer

Related Solutions

MATLAB: How we can amplify an image with nearest neighbour interpolation

MATLAB: [interp2] Different interpolation methods in each dimension.

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