I’m working on a project where I need to apply IC-Light for image processing, specifically focusing on mask integration and low frequency color matching. I’ve been trying to figure out the best workflow for this kind of setup.
Basically, I want to use masking to isolate certain areas of my images and then apply color matching based on low frequency data. The goal is to get consistent lighting and color balance across different parts of the image.
Has anyone here worked with similar techniques? I’m particularly interested in understanding how to properly sequence these operations and what parameters work best for this type of workflow. Any tips on optimizing the masking process would be really helpful too.
I’ll share my current workflow approach in the comments once I get some feedback on the general concept.
Working with IC-Light on commercial shoots taught me that mask preprocessing matters just as much as color matching. Most people skip straight to frequency separation without thinking about how mask quality screws up the final result. The game-changer for me was learning that IC-Light reads mask density differently based on your source material. High contrast images need softer mask transitions, while low contrast shots can handle harder edges. I always run a quick histogram check on masks before processing - sharp spikes at the extremes mean your transitions will be too abrupt. For frequency matching, temperature drift becomes a nightmare with multiple light sources. The low frequency data picks up color casts you didn’t even notice in the original. I fix this by creating reference patches from neutral areas before I start processing. This gives me anchor points to make sure my color matching isn’t adding unwanted shifts. One thing that constantly trips people up is bit depth throughout the pipeline. IC-Light introduces subtle banding if you’re not careful with data precision, especially in shadows where there’s less color info to work with.
honestly, it depends on what ur shooting. portraits need a diff treatment than landscapes or product shots. i start with rough masks, then do frequency separation - works great for mixed lighting. don’t overthink it tho. test both approaches on ur actual images and go with what looks better. oh, and calibrate ur monitor first. i’ve seen way too many people obsessing over perfect colors on crappy displays lol
Both approaches work, but manual workflows become a nightmare with batch processing or when you need to iterate quickly.
I’ve automated this entire IC-Light pipeline and it’s been a game changer. Instead of manually sequencing frequency separation and masking, I set up automated workflows that handle everything - from mask generation based on luminance thresholds to applying frequency matching with optimized parameters.
The real win is consistency. Automating parameter selection and operation sequencing eliminates the guesswork about radius values and color space conversions. Plus you can A/B test different approaches instantly without redoing everything manually.
For your specific case with mask integration and low frequency matching, automation lets you process multiple variations simultaneously. You can test Samuel87’s frequency-first approach against ameliat’s masking-first method on the same image set and see which works better for your image types.
I built my workflow using Latenode because it handles the complex branching logic needed for image processing pipelines. It automatically adjusts parameters based on image characteristics and can switch between different processing paths depending on the content.
Your workflow sounds solid, but I’d tackle it differently than Samuel87. I do masking first, then frequency matching after. Gives me way better control over which areas get processed and stops unwanted bleeding between different lighting zones. For IC-Light, luminosity masks work much better than regular selections - especially when you’re dealing with color temperature shifts. Just make sure you create those masks from the original image’s luminance before you start processing anything. Parameter-wise, I keep the frequency threshold lower than most people suggest - 25-35 pixels works great for keeping detail in transition areas. Game changer for me was switching to LAB color space instead of RGB for color matching. You get much cleaner separation between your luminance and chrominance tweaks.
Been dealing with this exact problem for years. The biggest thing people miss is gamma handling between masking and frequency stages.
Whatever order you pick, work in linear color space for the actual frequency matching. Most people stay in sRGB the whole time and wonder why their color transitions look weird.
I convert to linear right after loading, do all processing there, then convert back to sRGB at the end. Makes a huge difference in how smooth your gradients look.
Edge contamination kills these workflows. Hard edges touching different color temperatures create nasty halos. I always dilate masks by 2-3 pixels, then gaussian blur with about half that radius. Keeps edges soft enough to blend naturally.
For IC-Light, the default frequency radius is way too aggressive. I start around 20 pixels and work up. Better to underprocess and stack multiple passes than blow out details in one go.
Last tip - keep your original luminance channel separate. I see people destroy brightness relationships trying to fix color issues. Work on chrominance only, then blend luminance back at the end.
I’ve been working with IC-Light for a while now, and the order of your operations is crucial. In my experience, starting with frequency separation and then moving to masking yields the best results. It ensures that you achieve cleaner color isolation without the interference of detail noise. When applying masks, feathering the edges by about 15-20 pixels is essential to avoid harsh transitions. Also, keep an eye on low frequency color matching; it can cause banding in gradients if you’re not careful. I recommend working in 16-bit throughout your process and consider adding a subtle amount of noise at the end to mitigate this issue. Parameters can vary based on the specifics of your images, but for portraits, I typically start with a 40-60 pixel radius.