![]() ![]() But it's also quick and easy to make changes and see the results. ![]() You can see the before and after noise reduction and the preservation of details.Įvery photo is different, and your adjustments will vary from image to image. Here's an extreme enlargement of part of a photo taken just after sunset. Having said that, I never had to push those sliders very hard, because using the split screen feature and looking at the image at 400 percent magnification, I could easily find a setting where image grain (noise) was made invisible and I was not losing detail. I can report that if you use the sliders in DeNoise AI aggressively, you're going to lose some detail, add some halos, or increase sharpness to the degree that you may see some micro-image breakup. (It may be hard to see the improvement in these samples because I'm limited to small compressed files for uploading, but you can download large samples at the Topaz site.) You can see below how the sky is cleaned up in this enlargement without doing damage to the stars. Looking at those images, again, I found DeNoise AI significantly reduced the noise with no visible degradation of detail. I also do a lot of night photography, capturing the Milky Way, and I also do some telescope-based imaging. Camera sensors are getting better and better, but there was not a single image I fed DeNoise AI that was not improved, even when inspecting the image at high magnifications. Working in reduced light and at high ISOs are an invitation to noise. Most of my imaging is in low light, as I do almost all my landscape work around sunrise and sunset. With some detailed testing, I can say that Topaz DeNoise AI works better than any similar software I've ever tried. Its claim to fame is Topaz inspected thousands of noisy images and added AI smarts to help this new app/plugin know the difference between noise and actual data in an image. some sensors show luminance noise, others mostly chrominance noise).Over the years, I used the Topaz DeNoise plugin with fair to good results, and now, it's been updated to Topaz DeNoise AI. You statistically analyze those to find 1) which pixels are consistently bright or dark ("stuck pixels") and 2) any consistent patterns you can find in the noise so you can eliminate those directly (e.g., the part of the sensor near the processing may get warmer, and therefore noisier, than other parts), and 3) the type and degree of variation to expect from noise even where there isn't really a pattern (e.g. Normally, for this to work its best, you want to start with something like five dark frames. a 30 second exposure with the lens cap on) to get a better map of the exact noise characteristics of your exact sensor, and take that into account (I know Noise Ninja allows that, and if memory serves NeatImage does as well). IIRC, NeatImage also allows you to take "dark frames" (e.g. ![]() To compensate for that, the noise reducer will normally do rather minimal averaging in the green channel, somewhat more in the red channel, and more still in the blue channel.Īn advanced noise reducer will normally start with a model of the noise for an individual sensor, and apply the noise reduction based on that model. This, however, tends to increase the noise in the blue channel. To maintain the color balance in the final picture, the brightness of the blues in the picture has to be "boosted" to compensate. In a typical case, the green filter transmits more light than the red or (especially) the blue. The normal arrangement is something like g-r-g-b (aka, a Bayer pattern). A normal digital camera has a filter in front of each sensel. They will also take the channels of the picture into account. Something like NeatImage or Noise Ninja will do its pixel averaging adaptively - for example, it'll start with a scan for changes that occur over enough pixels that they're unlikely to be noise, and where it sees those, do the averaging over fewer pixels. Averaging fewer pixels loses less detail, but reduces the noise less. Averaging more pixels reduces noise more, but loses more detail. The problem, of course, is that simple averaging loses detail. At its most basic, noise reduction normally uses pixel averaging. ![]()
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