Because the Slanted Edge Method of 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?
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 relative regularized Line Spread Function (LSF) analytically in one go.
This front-loads all uncertainty to just the estimation of the ESF since the other steps on the way to the SFR become purely mechanical. In addition the monotonicity constraint puts some guardrails around the curve, keeping it on the straight and narrow without further effort.
This minimalist, what-you-see-is-what-you-get approach gets around the usual need for signal conditioning such as binning, finite difference calculations and other filtering, with their drawbacks and compensations. It has the potential to be further refined so consider it a hot-rod DIY kit. Even so it is an intuitively direct implementation of the method and it provides strong validation for Frans van den Bergh’s open source MTF Mapper, the undisputed king in this space,[1] as it produces very similar results with raw slanted edge captures. Continue reading Minimalist ESF, LSF, MTF by Monotonic Regression
) that reflects the mix of spectral information captured in the raw data, divorced from downstream color science; and
) that reflects the luminance channel of the image as neutrally displayed.