Hi Zach, > Hmm, this is worrisome. There really shouldn't be ringing on > continuous-tone images like Lena -- right? (And at no step in an > image like that should gaussian filtering be necessary if you're > doing spline interpolation -- also right?)
That's hard to say. Just because it's mainly a continuous-tone image doesn't necessarily mean it is well sampled everywhere. This depends both on the subject and the camera optics. Unlike the data I usually work with, I think everyday digital photographs (probably a photo scan in the case of Lena) do not generally have the detector sampling frequency matched to the optical resolution of the image. If that's true, the presence of aliasing in interpolated images depends on the structure of the subject and whether the scene has edges or high- frequency patterns in it. Stefan's rotated Lena example is indeed a bit bizarre on zooming in! However, the artefacts are clearly localized to distinct edges, so I suspect this is indeed some kind of aliasing. Moreover, it looks like Lena has been decimated (reduced in size) prior to the rotation. That is definitely a good way to get artefacts, unless an anti-aliasing filter is applied before shrinking the image. My impression is that this image is probably somewhat undersampled (to understand exactly what that means, read up on the Sampling Theorem). I can't say that the green blobs are *not* caused by a flaw in the algorithm. In particular, I am not used to working with colour images of this kind, so I don't have a good feeling for what aliasing looks like in a case like this. However, I definitely would not rule out intrinsic aliasing effects as the cause of this problem without investigating it further. One experiment might be to blur the original Lena with a Gaussian whose sigma is 1 pixel of the shrunken image before actually shrinking her, then do the rotation. > The first was on Stefan's artificial data which had sharp edges, and > got very nasty ringing artifacts even with 3rd order splines. From > your recollection, is this expected behavior based on splines and the > nature of Stefan's image, or more likely to be a bug? Your question was aimed at Travis, so I don't want to discourage him from answering it :-), but looking at this in more detail, I do think the amplitude of the artefacts here is greater than I might expect due to ringing with a quadratic b-spline kernel, which I think has minima with amplitudes <10% of the central peak. There has to be SOME oscillation, but in Stefan's "rotate_artifacts" example it seems to be at the level of ~100%. Also, it is not present on one of the inner edges for some reason. So I do wonder if the algorithm in nd_image is making this worse than it needs to be. These thoughts seem consistent with Travis's comments. Is it possible to transform the same data using the fitpack routines that Stefan mentioned in post 026641 and compare the results? I just tried doing a similar rotation in PyRAF on a monochrome image with a bicubic spline, and see considerably smaller artefacts (just a compact overshoot of probably a few % at the edge). Cheers, James. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
