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Digital image noise avoidance and removal

All digital cameras generate pixel noise, particularly in lower light conditions. Take a look at the image’s blue channel in particular and you’ll see significantly increased noise levels. This is because our eyes are less sensitive to blue light and therefore see less impact from the blue channel noise.

Sean at Cambridge Colour, outlines an interesting way to reduce noise by averaging that noise across multiple images. His article, one of many excellent tutorials on his site, covers a couple of ways to reduce image noise and offers an alternative I have not come across before: averaging multiple exposures using Photoshop layers. The techniques he describes, covering both how you take the shot in addition to post processing, reduces image noise while retaining and often actually enhancing image detail.

Sean’s site is also worth checking out for its other digital photography stunning images and some stunning images of Cambridge University.

Jen, on the other hand, takes a brief look at NeatImage Pro. A cost effective Photoshop plugin that, looking at the resulting images from both it and Sean’s averaging technique, might possibly use an automated form of image averaging to achieve sometimes surprising results. NeatImage’s strength is its ability to create profiles for your particular camera noise patterns - existing presets are available for download with the plugin.

2 Responses to “Digital image noise avoidance and removal”

  1. phil wig says:

    Interesting you found that site too. I use NI pro a lot. You don’t need it for A4 1ds images at ISO100, but it’s useful even for those with bigger crops/output.

    You probably know there are some little programs out there which stack astro images, but I hadn’t thought of using the same approach for night shots… interesting idea though.

  2. :) Pixel noise said to create unique camera “fingerprint”. Researchers at Binghamton University in New York have filed for a patent on a method of extracting unique identifying information from digital photos, based on pixel noise. According to the researchers, each camera generates its own digital “fingerprint” based on random variables that occur in the manufacturing process. By comparing multiple images from a single camera, the researchers say they can trace the images back to the specific camera that took the photos. The developers see potential for the method in law enforcement activities, such as tracking down child pornographers and forgers. We hope they’re right; certainly, any tools they can use are worth exploring. However, we can’t help but wonder whether clever criminals will just be able to run a few Photoshop filters and be able to wipe out any trace of their cam’s fingerprint before uploading their pics. Cool :)

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