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Hi I am trying out the superresolution samples on a simple input video (1280x720, 23.14 fps) with a duration of only 1 second and it is taking an excruciating amount of time to process (on the scale of minutes, instead of seconds) I changed the number of iterations down to 1 (instead of the default 180) which resulted in a somewhat better performance but still on the scale of minutes.

I had not compiled opencv for CUDA or OCL but I do not expect the net gain to be of such that the whole thing runs in seconds.

Question to those with experience with superresolution: Is this the expected behavior? If so perhaps it should be added to the documentation? If a much faster performance is expected, could you kindly provide with some hints?

best regards

of course according to Zhang's paper it seems more samples the better the results. In fact according to the data presented there (if I remember correctly) there are dramatic improvements with 5+ images. However in many tests I got a much lower reprojection error rate (~.2 or .3) when I used 5-10 images versus 20-30. In my view it ought be possible for calibration in a controlled environment to use images taken from a minimal set of fixed orientations to get a reliable result. Instead of the adhoc approach currently. That needs re investigating the mathematical derivation. As far as near or far camera positions relative to the chess board plane: I got my best results (better undistortion) when all the pictures were taken with the camera closest to the plane. Thanks.

Hi, I finally manged to get camera calibration working with relatively reliable results (using a set of 10 standard chessboard images). However the choice of the orientation of the chess board seems rather ad hoc. Does anyone in this forum know of a minimal set of standard orientations (e.g. n=(0, 0, 1), (0, -1, 1), (-1, 0,1) ...) which would result in a stable camera calibration? In a controlled environment this ought be possible? In the provided examples it seems like people hold the board in front of the camera and hope for the best.

Thank you