Overview
Conventional noise reduction has one fundamental problem: at the pixel level, noise and fine detail look similar, so a filter that removes one removes the other. Push it far enough to clean a high sensitivity frame and skin becomes plastic, foliage becomes a green smear and fabric loses its weave. Everybody who has shot in poor light knows exactly where that threshold sits and has learned to stop short of it.
This program approaches it differently, using models trained on pairs of noisy and clean frames to distinguish the two by context rather than by local statistics. In practice it means a frame shot several stops beyond what used to be usable comes back with the grain gone and the detail intact, which changes what is worth shooting rather than just what is worth keeping.
The models and when to use each
Four processing models are included and they behave differently enough that trying more than one is always worth the seconds it takes. The standard model is the general purpose choice and handles most frames. The clear model is more aggressive and suits images where the noise is severe and the subject has relatively simple texture. The low light model is tuned specifically for high sensitivity frames with colour blotching in the shadows, which is the failure mode that ruins most indoor and night photography.
The severe noise model exists for the frames you would otherwise throw away, shot at the top of a sensor's range or heavily underexposed and lifted. It works harder and occasionally invents texture that was not there, which on a frame that was otherwise unusable is an acceptable trade and on a good frame is not.
The comparison view puts up to four variants side by side at pixel level, which is the fastest way to choose. Auto mode picks a model and settings per image and is right often enough to be a reasonable starting point for a batch.
Controls and detail recovery
Beyond the model choice there are two main sliders: how much noise to remove, and how much detail to recover afterwards. The second is the interesting one, since it reconstructs edge definition that the noise was masking rather than simply sharpening. Overdoing it produces a crunchy artificial look, so it wants a light hand.
Colour noise is handled separately from luminance noise, which matters because they usually need different amounts. Original detail recovery brings back a proportion of the source frame in areas the model over smoothed, blended by a mask, which is the safety valve for the occasional frame where the model has been too enthusiastic about a texture it did not recognise.
Raw files can be processed before demosaicing, which produces a measurably better result than working on an already developed image, since the noise is being addressed while it is still in its original form rather than after it has been spread across the colour channels.
Batch work and where it fits in a workflow
The batch panel processes a folder with a chosen model and settings, or with auto mode deciding per image. On a set from one event shot under one light, fixing one frame and applying it to the rest is the efficient route. Output goes to a chosen folder with a naming pattern and a format of your choice, including high bit depth for further editing.
As a plug-in it installs into the standard host filter paths and returns the result to the layer stack, which is the arrangement to use when noise reduction is one step in a longer retouch. In a raw workflow, this should run early, before sharpening and before any local adjustments, since both of those interact badly with noise that has not been dealt with yet.
Processing is graphics card accelerated and the speed difference between a card with a decent amount of video memory and one without is substantial. On a large batch it is the difference between a coffee and an afternoon.
What you get
- Four processing models for different noise situations
- Model designed specifically for high sensitivity shadow blotching
- Severe noise model for frames that would otherwise be discarded
- Side by side comparison of up to four variants at pixel level
- Auto mode selecting a model and settings per image
- Separate colour and luminance noise handling
- Detail recovery that reconstructs edges rather than sharpening
- Original detail recovery blended by mask as a safety valve
- Raw processing before demosaicing for a better result
- Batch processing and host plug-in in the same install
Inside the archive
- DeNoise AI standalone application, 64-bit
- Complete model set installed locally
- Host plug-in for the standard filter paths
- Example images demonstrating each model
- Notes on where to place it in a raw workflow
System requirements
| Operating system | Windows 10 version 21H2 or later, Windows 11, 64-bit |
|---|---|
| Processor | Intel or AMD with AVX support |
| Memory | 8 GB minimum, 16 GB for batch work on large raw files |
| Graphics | 4 GB VRAM minimum, 8 GB strongly recommended for speed |
| Storage | 6 GB free for the application and models |
| Display | 1920 by 1080 or better for the comparison view |
Installing it
- Unpack the archive to a local folder.
- Suspend real time protection for the length of the install.
- Run the installer as administrator and let it place the plug-in if a host is detected.
- Launch the standalone once so the models finish unpacking.
- Process one test frame and confirm the export carries no watermark.
Mirrors
| Route | Region | Note | State |
|---|---|---|---|
| Direct, primary | Europe | No wait, resumable | Online |
| Direct, secondary | North America | No wait, resumable | Online |
| Torrent magnet | Global | Either works at this size | Online |
Release history
- Low light model retrained, better colour blotch handling in deep shadows
- Fixed batch processing skipping files with unusual raw extensions
- Graphics card memory handling improved on cards with 4 GB
- Comparison view extended to four simultaneous variants
- Original detail recovery mask made editable
- Raw pre demosaic processing added for more camera formats
- Reduced processing time on frames above 40 megapixels
Questions about this release
Which model should I use?
Start with standard, and try low light on high sensitivity frames with shadow blotching. The comparison view answers this faster than any rule of thumb.
Does it work on raw files?
Yes, and processing before demosaicing gives a better result than working on an already developed image.
Where does it belong in my workflow?
Early. Before sharpening and before local adjustments, both of which interact badly with unaddressed noise.
Are exports watermarked?
No. The licensing is applied in this build and output is clean at full resolution and bit depth.
Why is it slow on my machine?
Video memory. Processing is graphics card accelerated and a card with 4 GB or less will fall back to a much slower path on large frames.
Comments
Running it before demosaic makes a visible difference. Worth reordering the workflow for.
4 GB card handling is better in this build. Still slow on huge files but it no longer falls over.
Comments are read before they appear. If a build stops working, say so here and it gets rebuilt rather than quietly left up.
Low light model on a set from a dim venue. Frames I had written off are usable now, no exaggeration.