I’ve been able to experiment how, on old/faded slides, NLP makes quite a good job.
Faded colors are being revcovered, not perfectly for sure, but much better than when using per color tonality adjustmets in LR or Photoshop.
I haven’t tried on new/recently shooted and developped slides.
Here are the steps I follows:
1/ Using Lightroom, invert the tonality curve so as to obtain a virtual negative
2/ Launch NLP and “Convert” as if it was a real negative
3/ Make any suitable adjustments using the NLP cursors
4/ Fine adjust if needed in LR (It may be noted that LR cursors do continue to function as usual)
This new “Convert positive” feature would require NLP, I suppose:
to incorporate the tonality curve inversion step,
to add “Convert positive” to the Convert menu.
I may miss something naturally, but after having converted several tenth of old/faded positives/slides using the above steps, it looks as it could be a plus for many of us trying to save old archives from unavoidable deliquescence.
It may also perhaps provide an interesting workflow for scanning positives using the NLP color synchronization features.
I tried to reproduce the steps you suggested → inverting the slide within LR → applying NLP.
I found a significant difference concerning the colours of the slide presented by LR and the ones
presented by NLP after converting the slide’s curve and applying NLP’s conversion.
I think the reason ist that any colour negative (as far as I know) has the typical orange-brown mask all over and also “beneath” the colours of the photo. And any conversion tool processing a coloured negative
must consider this mask.
But converting the curve of a slide creates a result which misses this orange colour mask.
And IMHO this is the reason that the slide’s positive colour is different from the one created by NLP from a “faked” negative. I cannot see how this problem could be solved.
By the way - after scanning a slide or having created a digital copy of it by a DSLR you can completely
process it within LR using all tools without needing any plug-in - provided you got a RAW.
@Digitizer first as the above suggested link has been my source for testing this idea.
@Dietrich. Indeed, when I do invert a positive, the resulting pseudo negative looks very much like a true negative after having modified the color temp by using the image borders, and after having cropped the borders.
Therefore, I do suppose that the “color temp modified” image mostly removes the orange mask?
Nevertheless, we are talking about so much faded or “color casted” images that anything helps. I’ve been able to observe that NLP most of the time makes a much better than me playing with color temp, HSL, Color Grading, and so on.
But not always.
I’ve DSLR scanned 200 slides from 1978 this afternoon, and used the “pseudo negative” method on all of them, just to see what happens.
85% of the scanned slides do directly improve providing more natural colors and tonalities compared to the direct scan.
I’ve but observed that I do get bad or very bad results when the original slide colors are not “standard” i.e too cold or too warm global tonalities. In that case, NLP has no clue and the provided result is then worst than the orignal direct positive.
I would say that it’s anyway worth a try when you have to deal with such old material.
if your method produces good results for you it’s okay.
For a presentation I digitalized lots of coloured slides (format 6 * 6 cm) with a Canon EOS 7D
from a lightpad (=> RAW-files).
Concerning further processing I could do anything within LR (especially modifying white balance) to get the pictures I needed. At least 90% of the slides (from 1992 up to 1997) still had excellent colours.
A slide allows you to compare directly the colours and contrasts of the digital image with
the original image.
For b&w negatives (also in 6*6) I use NLP exclusively because it does a much better job than LR alone.
Thanks so much for the tip! I inherited 750 color slides from my dad from the 50’s and 60’s, and indeed, many are a bit of a red soup. I made a book from a selection to preserve what could be rescued, but spent hours and hours trying to recover the slides. I was amazed how much can be recovered, but it’s tedious doing it manually. I tried now with NLP as you describe and it takes only a mere seconds for what takes many many minutes by hand. Maybe it’s not perfect, but I’ll take any time saving I can get, and after NLP and a few fast tweaks in LR to tune it further I’m getting better results than the labor intensive manual process.
This is yes the same for me: This alternative process is not intended as “The positive scanning solution”, but works quite well to simply salvage old faded photos in a few minutes instead of spending too much time with LR cursors.