Journal paper on MTF Mapper's robust edge-spread function construction algorithms now available

A new paper on MTF Mapper's robust edge-spread function (ESF) construction algorithms has been published in the Optical Society of America's JOSA A journal. The paper provides an analysis of the impact of the slanted-edge orientation angle on the uniformity (or lack thereof) of the distribution of the samples used to construct the ESF; this is essentially the evolution of the notion of critical angles first described in this blog post. Next, the paper describes two different methods that can be used to construct an ESF to minimize the impact of the non-uniformity of the samples, with some results to demonstrate the efficacy of the proposed methods.

The full citation for this paper is:
F. van den Bergh, "Robust edge-spread function construction methods to counter poor sample spacing uniformity in the slanted-edge method," Journal of the Optical Society of America A, Vol. 36, Issue 7, pp. 1126-1136, 2019.

You can see the abstract of the paywalled article here. Or you can go and take a look on SourceForge, where you can find pre-press versions of my MTF Mapper related papers.

I am particularly fond of the LOESS-based algorithm, which is available in MTF Mapper version 0.7.16 and later (on SourceForge now). The LOESS-based algorithm performs better than the current implementation at higher frequencies, with less bias in the MTF curve above 0.5 cycles per pixel. This does not result in a huge improvement in the accuracy of MTF50 values; for practical use the main advantage of the LOESS-based algorithm is that it is able to produce more consistent results regardless of the slanted edge orientation angle. As an example of the improvement you can expect with the LOESS-based algorithm, the following figure illustrates the difference between the legacy ESF model (now called "kernel" in the preferences), and the LOESS ESF model (called "loess" in the preferences):

The error between the expected analytical SFR and the legacy ESF model (black curve), compared to the error obtained with the LOESS model (red curve), as measured on a simulated slanted edge. The legacy ESF model tends to overestimate contrast at high frequencies; the LOESS ESF model no longer does this. Note the scale of the y-axis!

Initially, I plan on making the use of the new algorithm optional (0.7.16 still defaults to "kernel"), but I hope to make the LOESS-based algorithm the default in versions 0.8.0 and later, after it has endured some more real-world testing. Any feedback will be appreciated!