MOMS (maximal-order-minimal-support) functions give the least number of supports for a given approximation order L. The stringent requirement of number of supports is critical to real-time signal processing, which is why sinc (the kernel that gives ideal reconstruction) is not used in practice. B-spline based interpolating kernels are usually used in spline interpolation. MOMS functions are constructed by b-spline functions. Here we provide an implementation of O-MOMS (optimal MOMS), which outperforms b-spline kernels of the same degree.
This implementation uses DTFT to compute the coefficients in the prefiltering step [Thevenaz 2000]. For boundary condition, we assume periodic, which is scheduled to be changed to mirroring in the next release.
Degrees of 0 through 5 are supported.
T. Blu et al.
Minimal Support Interpolators with Optimum Approximation Properties, ICIP 1998;
MOMS: Maximal-Order Interpolation of Minimal Support, IEEE Transactions on Image Processing Vol. 10, No.7, 2001;
Phillippe Thevenaz et al.
Interpolation Revisited, IEEE Transactions on Medical Imaging, Vol. 19, No. 7, 2000.
Meng Wang (2021). O-MOMS Supersampling routine (https://www.mathworks.com/matlabcentral/fileexchange/26301-o-moms-supersampling-routine), MATLAB Central File Exchange. Retrieved .
He ask me the toolbox images, is it normal ?
Ok. Simple example.
Sr = momssupersample(S.', 4, 5);
Could you help me reproduce this weird thing? Thanks.
The strange behaviour of the function.
If you make a resampling, and then make the binning and again resampling , there will be offset relative to the original series!
A - original
B-resempled and binned
max (A) index 70 106
max (B) index 69 105
At resempled images (128.128 -> 4096.4096)
max (A) index 2222 3361
max (B) index 2207 3345
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