I'm trying to guess what noise reduction algorithms are used in commercial processors for raw images from digital cameras. I find this fairly easy to do for the sharpening algorithms (most use unsharp mask as at least one option, for example, and this is clear from the behavior and adjustable parameters), but I know next to nothing about noise reduction. I would like to
- identify as well as possible the basic algorithms being used (of course there will be some proprietary optimizations), and
- identify the corresponding academic papers that derive them, to learn their inner workings and use as a springboard to the contemporary literature (via tracking citations).
The parameters for the noise reduction algorithms in two common processors, Capture One and Lightroom, are shown below. Here are some further clues.
- They're fast. Each works in about a second or less on my modest laptop on 24 MP raw images.
- They're old. Lightroom was released in 2007, and I don't think the core options have changed since then.
- They operate separately on luminance and color noise.
- I don't think they're wavelet-based, since (as far as I know) these applications do not use wavelets anywhere else.
Question 1: Is there a well-known, industry-standard algorithm that meets this description?
Among all raw processors available, the best noise reduction seems to come from DxO's Photolab. (This is of course debatable, but it consistently places at or near the top of comparisons I've seen.) I'd like to try to understand how it operates. A screenshot of its parameters is attached.
DxO describes it as a local algorithm that works pixel-by-pixel:
“the denoising algorithms (in PRIME) analyze the structure of Raw images in depth: more than a thousand neighboring pixels are surveyed for each pixel. This extensive exploration identifies similar data (for) use (in reconstructing) image information.”
Besides that, we know a few more things:
- It's relatively new (released 2014).
- It's slow. Users report it takes a minute or more per photo.
- It's recommended for very noisy, high ISO photos. DxO and users report no advantage over faster noise reduction techniques for photos with mild noise.
Question 2: What algorithm could DxO plausibly be using?
I realize this is a somewhat unusual question, so thanks in advance for letting me access your expertise.