Comment by mattz56

I don't have a public writeup, but the general approach for orange mask removal is: scan a blank unexposed frame from the same roll, take its per-channel mean as your reference white, then divide each pixel by that reference per channel before doing anything else. That normalizes the mask out instead of trying to subtract a fixed color. After that, a per-roll linear stretch (black point from the darkest scanned frame, white point from the lightest) gets you a rough base. Tone curve is the hard part: NLP uses a nonlinear S-curve tuned per stock, and matching that from scratch means a lot of trial and error with reference images you know the expected look of. If you want a quick win before rewriting things, Lightroom lets you write develop settings to XMP so you're not locked into the catalog even if you cancel later; the actual RAW/TIFF processing engine is what needs a subscription, not the metadata.