Image noise reduction

The physical and human spects dealing with audio, video, and images Vision and color perception: objectively describing color · the eyes and the brain · physics, numbers, and (non)linearity · color spaces · references, links, and unsorted stuff Image: file formats · noise reduction · halftoning, dithering · illuminant correction · whole-image descriptors · image feature and contour detection · OCR Video: format notes · encoding notes · On display speed Audio physics and physiology: Basic sound physics · Human hearing, psychoacoustics · Descriptions used for sound and music Digital sound and processing: capture, storage, reproduction · programming and codescs · some glossary · Audio and signal processing - unsorted stuff Electronic music: Some history, ways of making noises · Gaming synth · on APIs (and latency) ··· microphones · studio and stage notes · Effects · sync · Music electronics: device voltage and impedance, audio and otherwise · amps and speakers · basic audio hacks · Simple ADCs and DACs · digital audio · multichannel and surround · 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 Unsorted: Visuals DIY · Signal analysis, modeling, processing (some audio, some more generic) · Music fingerprinting and identification 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, fix, or tell me)

gaussian blur

(or other simple interpolating blurs)

Upsides:

• Simple. Fairly fast.
• does not introduce spurious detail

Downsides:

• indiscriminantly removes (high-)frequency content. a.k.a. "Smears everything"

median filtering

Upsides:

• 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

Downsides:

• 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.

Upsides:

• This tends to look more detailed than a basic mean filter, particularly on sharp images

Downsides:

• Can't really tell what real edges are; for subtler images it can be much like mean

See also:

• http://en.wikipedia.org/wiki/Total_variation_denoising
• Playing with:
  • http://svaiter.github.io/pyprox/auto_examples/plot_tv_denoising_lena.html
  • http://scikit-image.org/docs/dev/api/skimage.filter.html#denoise-tv-chambolle

bilateral denoise

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.

Playing with:

• http://scikit-image.org/docs/dev/auto_examples/plot_denoise.html
• http://scikit-image.org/docs/dev/api/skimage.restoration.html#denoise-bilateral

non-local means denoising

See also:

• https://en.wikipedia.org/wiki/Non-local_means

Anisotropic diffusion

See also:

• https://en.wikipedia.org/wiki/Anisotropic_diffusion

Wiener filter

See also:

• http://www.cs.tau.ac.il/~turkel/notes/wiener_theory.html
• https://www.clear.rice.edu/elec431/projects95/lords/wiener.html
• http://blogs.mathworks.com/steve/2007/11/02/image-deblurring-wiener-filter/
• http://en.wikipedia.org/wiki/Wiener_filter