  Many applications in image processing require resampling of arbitrarily
located samples onto regular grid positions. This is important in frame-rate
up-conversion, super-resolution, and image warping among others. A
state-of-the-art high quality model-based resampling technique is
frequency-selective mesh-to-grid resampling which requires pre-estimation of
key points. In this paper, we propose a new key point agnostic
frequency-selective mesh-to-grid resampling that does not depend on
pre-estimated key points. Hence, the number of data points that are included is
reduced drastically and the run time decreases significantly. To compensate for
the key points, a spectral weighting function is introduced that models the
optical transfer function in order to favor low frequencies more than high
ones. Thereby, resampling artefacts like ringing are supressed reliably and the
resampling quality increases. On average, the new AFSMR is conceptually simpler
and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid
resampling while being approximately 14.5 times faster.
