Hi, Some time ago, I raised Issue #929 on github about the interpolation step in skimage.transforms.radon_transform.iradon being too slow. After some on-and-off investigation, I found two opportunities to speed this up. First, numpy's linear interpolation routine interp() was very slow for repeated interpolations of well-ordered input -- this is now fixed in the github master repo for numy, and gives roughly a 3x to 4x speed-up. Second, the trigonometric calculations can be cached when doing repeated calls to iradon() for the same geometry (image size, number of angles). This can give another factor of 1.5 to 2.0x speed-up. This is PR #1474. Some preliminary timing results: Python2.7.8 (linux), with an image shape of (500, 500), and sinogram shape of (500, 361). I used iradon() options of dict(filter='shepp-logan', interpolation='linear') and tried both circle=True and False. The "workspace?" column here indicates whether the iradon_workspace() function introduced in PR1474 was used. with circle=True: numpy skimage workspace? Best, Worst of 5 (s) |---------+-----------+------------+---------------------| 1.9.2 master N/A 6.085 6.132 master master N/A 1.784, 1.813 master PR1474 No 1.788, 1.809 master PR1474 Yes 1.103, 1.160 |---------+-----------+------------+---------------------| with circle=False: numpy skimage workspace? Best, Worst of 5 (s) |---------+-----------+------------+---------------------| 1.9.2 master N/A 2.736, 2.767 master master N/A 0.820, 0.831 master PR1474 No 0.802, 0.814 master PR1474 Yes 0.535, 0.540 |---------+-----------+------------+---------------------| That is, the cumulative improvement is about 5x. Also note that not using the workspace reverts to the older behavior, and that using he workspace for 1 run of iradon() has basically no improvement over not using the workspace. Oddly, iradon() is now faster than radon() on this machine/image size (radon() takes about 3.5 sec). I don't understand why that would be. Anybody understand why radon() is so slow? Cheers, --Matt Newville