Different color results between adjacent, near-identical shots

  1. NLP Version 3.0.2

  2. DSLR scanning using 1) Nikon D750 2) Nikon 55mm F/2.8 Micro AIS Manual Focus Lens 3) VALOI easy35

I recently scanned and converted these two negatives with NLP and was surprised to see different color interpretations from what is essentially the same shot. These frames are adjacent on the roll and I don’t believe the camera settings were changed, but full disclosure, these shots are from roughly 2004 or 2005, so I can’t quite remember :sweat_smile: I converted them at the same time and used Roll Analysis. They’re both a little dark; if I recall correctly, I was shooting our reflection in some sort of sculpture in DC. But I’m more confused about the color difference between them. The first one looks okay and I’ve been able to work with it; the second one has a noticeable green/cyan cast.

Any ideas on why these would come out so differently?

Welcome to the forum @JoeyPasco

The two images show black and white borders. Did you crop them off before the conversion?

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I used the border buffer (set to 10%) prior to conversion. That should do the trick, right? My understanding is that’s the purpose of the border buffer.

It should do the trick…but only if the borders are not shown if you turn on the border buffer preview.

Images with only a few colours tend to react more wildly to the conversion. Nevertheless, the images look like you’ll have to experiment with different first- and second-tab settings of NLP.

If you like, you can post a link to a share with the two original (scanned) .NEF files for the rest of us to try our luck. NLP can make great conversions and sometimes they are just what they are: starting points for further testing or tweaking.

So far, I’ve never used roll analysis (mostly because it is a new feature) and most of my scans are exposed adaptively and therefore don’t agree to roll analysis anyways. Re-converting old test conversions with roll analysis did bring up better colours at times, and at other times, some of the images turned out completely off.

NLP has so many choices of settings and sliders that it takes some trial&error to get the desired results more easily and rapidly. And as we all know, the treasure is buried near the end of the rainbow :wink:

Difficult images:

Top row: white balanced scans
Middle row: conversions without roll analysis
Bottom row: conversions with roll analysis

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Thanks for this! I tried white balancing the negative prior to conversion and then re-converting without roll analysis, and I got a much better result :slightly_smiling_face: I really appreciate your help!

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…sometimes, it pays to read the manual or the info that is presented otherwise like this one:


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I had seen that, but it was my understanding that this only applied to previous versions of NLP, prior to the implementation of Roll Analysis, and that using the Border Buffer feature precluded the necessity of this task.

When RA first came out, I used it a lot, but I find I get better results 9.5/10 not using it. I’ll normally convert using RA but then, once converted, in the ‘roll tab’ I’ll go back to “Individual Image” and I’ll only go back to the RA setting on some of the frames that seem to go completely wonky in “Individual Image” for whatever reason.

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I think this is a good idea, and it’s simple enough to switch back and forth to find what you like better.