Image noise reduction
| The physical and human spects dealing with audio, video, and images
Noise stuff: Stray signals and noise · sound-related noise names · electronic non-coupled noise names · electronic coupled noise · ground loop · strategies to avoid coupled noise · Sampling, reproduction, and transmission distortions · (tape) noise reduction
device voltage and impedance, audio and otherwise ·
amps and speakers ·
basic audio hacks ·
Simple ADCs and DACs ·
digital audio ·
multichannel and surround
For more, see Category:Audio, video, images
|This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, or tell me)|
(or other simple interpolating blurs)
- Simple. Fairly fast.
- does not introduce spurious detail
- indiscriminantly removes (high-)frequency content. a.k.a. "Smears everything"
- Simple. Not quite as fast as you'ld think.
- rejects outliers; best example is rejecting salt and pepper noise
- will preserve edges better than e.g. linear interpolation
- can remove high-frequency signal
- the edge preservation depends on some conditions, so doesn't always happen. The mix can look odd.
total variation denoising
Varies amount of blur by the amount of variation near the pixel.
Which means it mostly lessens noise in otherwise flat regions, while leaving spikes and edges mostly intact.
- This tends to look more detailed than a basic mean filter, particularly on sharp images
- Can't really tell what real edges are; for subtler images it can be much like mean
- Playing with:
Reduce noise while preserving edges.
Averages based on their spatial closeness and radiometric similarity, and potentially other metrics. Like total-variance denoising in that it easily preserves edges, yet is often more true to photographic original than.
non-local means denoising