Why use NLP over pure Photoshop?

There’s a frequent debate on photography forums about whether Negative Lab Pro is worth the investment. Here’s my take on it. I understand that majority of folks here are proud owners of the license already, but may be someone still need convincing. Besides, if my understanding of the process is not correct , @nate will correct me and add missing details, thanks!

To really grasp the value of Negative Lab Pro, it helps to understand the typical process of digital negative conversion. We’ll skip the granular details to focus on what really matters.

Let’s review the negative inversion process in general terms, step by step, to better comprehend the value the discussed tools bring in. The walkthrough below is based on what one would do in Adobe Photoshop.

First up, the color negative is photographed with a digital camera in RAW mode. Once the RAW image is imported into an image processor like Photoshop, it’s crucial to set the gamma to 1 instead of the more common 2.2. This preserves the linearity of the RAW capture and optimizes the sensor’s linear response—crucial for rendering the darks effectively in your final image, a step often glossed over in most guides.

Next, we normalize the luminance range for each color channel. This involves adjusting the curves so the darkest pixels map to values between 0-3 and the brightest between 253-255, proportionally adjusting all values in between. In Photoshop, this means finding the darkest and brightest spots (avoiding specular highlights) and tweaking the curves channel by channel. What you end up with is a normalized RGB image of the negative.

Applying an inversion layer brings us to the positive image, which initially appears flat with subdued colors. Adjusting the gamma curve at this stage enhances the image’s richness and contrast. Although the image may not yet be perfect, processing in 16-bit mode should prevent banding, and further corrections can neutralize grays among other refinements.

Regardless of the software used, the steps are generally consistent—the main differences lie in the time spent and the specific enhancements each program applies to optimize the final image. Software tools ideally exclude the rebate and edge areas to avoid skewing the conversion due to internal reflections from the original film camera or the film holder during scanning.

In Photoshop, this entire process can be done manually or automated with actions. However, tools like the very good Grain2pixel Photoshop plugin (Download – Grain2Pixel) , which is free and supports batch processing, simplify these steps immensely.

An older, yet still useful tool is the Negafix script, which employs the Image Magick open-source library. It’s quite transparent, allowing users to see all conversion steps and parameters used, producing surprisingly decent results.

When I sought ChatGPT’s assistance for conversion, it provided a Python script that works quite effectively, available here: GitHub - vsaddr-github/neg2pos: Converting negative image into positive image and very short demo video is available here: https://youtu.be/vczMHo_2wiM

Turning to Negative Lab Pro, it essentially performs similar normalization and conversion processes within Lightroom. Its inner workings remain proprietary, but it’s clear that NLP offers more than just basic conversions, supporting various aesthetic looks like cinematic or flat. Its simplicity, efficiency, and the high-quality results it produces make it a favorite, despite the initial cost.

The choice to invest in NLP should consider your volume of film work. For those processing and digitizing a couple of rolls per year, the traditional lab route might be more economical and hassle-free. However, for anyone digitizing two or more rolls per month, setting up a home digitizing workflow becomes justifiable.

Suppose your personal labor costs are $20 per hour, and NLP saves you about three minutes per frame; that’s roughly $1 saved per frame. After just three rolls of film, NLP pays for itself.

While there’s a learning curve involved in mastering exposure, choosing the right backlight, and applying finishing touches, the time and quality efficiency of NLP likely makes it a worthy addition to your film digitization toolkit.


Having futzed around in Photoshop and other applications before NLP came along including doing everything you say because it is basically correct, I quickly discovered that NLP is a God-send. Wouldn’t look back