I’ve been using NLP for quite a few years now. First with my Plustek and Vuescan. Now with a Valoi Easy35. Throughout all the different versions I have found that NLP can be quite inconsistent with WB, whatever scanning method I use.
Currently my main 2 points are:
Auto-Warm is probably 99% a total orange mess.
The newer Auto-Mix is an improvement on Auto-Neutral but often introduces a notable green cast to the image, especially in the highlights.
Does anyone else find this?
NB: My NLP is correctly set up and I know about WB off the film border.
Getting correct white balance can depend on a variety of factors. Do you develop yourself or use a lab? I have found that even minor processing errors can have an effect on the auto wb interpretations. Even the best negs/scans will probably need a touch of manual white balancing here and there.
I always use auto neutral as a baseline and then work off that. I’ve found the other auto options to be relatively useless. But if even after tweaks to wb and tint things still look way off you might have a conversion interpretation issue or processing problems.
@mattdoerr , I work the same way as @winslow … and I don’t care whether WB is accurate or correct, as long as I can make an image look how I want it to look. Unless it’s about reproductions, “correct” or “accurate” are concepts that are best left behind imo.
Selecting one of the WB presets on NLP’s second tab can bring an image closer to target or take it away from it. And from what I understand from NLP’s inner workings, this is to be expected and dependent on chroma and luma characteristics of the source image.
If we look at the tone curves of a converted image, we can see flat and/or steep curves and a slight change of balance can make a big difference. If we’re lucky, NLP will match our intentions and more often than not, it requires additional adjustments - at least that is what I see with the negatives I have. Most of them are about 40 years old, taken under various lighting conditions and using all kinds of film.