Because the Slanted Edge Method (for estimating the Spectral Frequency Response of a camera and lens) is one of the more popular articles on this site, I have fielded variations on the following question many times over the past ten years:

How do you go from the intensity cloud  produced by the projection of a slanted edge captured in a raw file to a good estimate of the relevant Line Spread Function – while touching the data as little as possible so as to be confident in the absolute value of the resulting SFR?

So I decided to write down the answer that I have settled on.  It relies on monotone spline regression to obtain an Edge Spread Function (ESF) and then reuses the parameters of the regression to infer the Line Spread Function (LSF) analytically.  This front-loads all uncertainty to just the estimation of the ESF from raw data since the other steps on the way to the SFR (derivative and Fast Fourier Transform) become purely mechanical.

This minimalist, what-you-see-is-what-you-get approach gets around the usual need for signal conditioning such as binning and finite difference calculations, with their drawbacks and compensations.  The approach has the potential to be further refined so consider it a hot-rod proof of concept.  Even so it is an intuitively direct implementation of the method and it provides strong validation of Frans van den Bergh’s MTF Mapper, the undisputed champ in this space,^[1] as it produces very similar results with slanted edges captured with good technique in a raw file. Continue reading Minimalist ESF, LSF, MTF by Monotonic Regression →