I just saw this thread; I posted about this topic in a different thread a few days ago.
My experience so far has been very different. Shooting with the Big Scanlight in RGB mode, I am getting results that are so far off that I can’t even figure out the correction to get them to look right (at least, within NLP itself; I do think I could get it there with Lightroom adjustments). Principally, the saturation is unnaturally high and the colors are skewed magenta. I also did not find that I could skip the white balance step, though I do agree that the shift needed is smaller.
I’m curious if there’s something different about my setup. Is there some particular film stock you’re using that you’re getting great results with? Most of the negs I’ve tried have been Portra/Ektacolor 400, but I have tried some older stuff from my archives and had similar problems. I’ve gone back to just using it in white mode rather than RGB mode for now, because at least that gives closed to natural results.
I’d like to revise what I said in my post, because this stuck with me and I went back into my office to do some more testing. What I found is that, while the RGB light does not completely cancel out the orange mask (doing a white balance still creates a noticeable shift), it does work to run NLP on the negs without doing a white balance first. And the result might be a bit better (though, at least on the couple of sample frames I tried, it wasn’t radically different). So that might be something I will play with some more.
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Using NLP with an RGB source can result in weirdness. I find it is much better to do straight inversions and color corrections with the Scanlight scans without any special inversion algorithms. I use C1, but I’m sure a fast and efficient workflow could be built in LR as well.
Do you white balance first? Or just invert before any other adjustments?
Yes, I white balance the whole roll or batch using a blank frame.
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Alright, this was a great tip. Just white balancing and manually inverting without involving NLP at all works very well. Thank you for suggesting this!
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