To start, shoot multiple RAW exposures of an unchanging scene that would benefit from significant noise reduction. That’s the idea behind averaging multiple exposures to first identify and then eliminate noise. Since noise is random and changes with every shot, it makes sense that if you made multiple exposures of a scene and all of the actual image-forming detail remained the same from frame to frame and the noisy pixels would be different in every frame, you could analyze the exposures as a group to better determine which pixels comprise image-forming detail and which ones are just noise. That’s the real challenge for any kind of noise reduction-separating the signal (the image-forming detail) from the noise. There are plenty of traditional approaches to eliminating noise-from using Photoshop’s Reduce Noise filter to standalone programs like Noise Ninja and Neat Image, as well as RAW converters and their noise reduction features-but all of them risk compromising the image quality by removing actual image-forming detail. So, removing excessive noise-literally improving the signal-to-noise ratio-is as important as ever. As great as digital camera sensors have become at combating noise, this film-grain-reminiscent interference can still rear its ugly head at high ISOs and in long exposures.
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