![]() ![]() some sensors show luminance noise, others mostly chrominance noise). ![]() 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. in my opinion neat image (with 'filter and sharpen' preset) works much better in filtering out the noise. 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. Image Detail is not blurred, if the images are perfectly aligned.Īveraging can be performed with Photoshop or any other image editor capable of layers by placing the images in layers and setting layer opacity to decreasing values (from top to bottom layer): 50% for top layer, 33% for second layer, 25% for third layer, 20% (=1/5), 17%(=1/6), 14%(=1/7), 13%(=1/8) and so on.At its most basic, noise reduction normally uses pixel averaging. This is you need 4 images to double the s/n ratio and 16 images to have s/n ratio 4 times as high as in one image. With this technique the signal to noise (s/n) ratio increases by the square root of the number of images. Available either as a command-line toolĭcraw has a command-line option (-n noise_threshold) to erase noise using wavelets.Īdding up several images - either different scans from the same slide/negative or several digital shots from the same subject - are based on the fact that digital noise is random and different in each image but image detail should be the same. Use the anisotropic filter or any of the other ones to reduce noise. Open source and very powerful filter collection. Helicon Filters are shareware with a free edition limited in functionality. ![]() Grain surgery's home page is Helicon Filter Grain surgery can add, remove or match grain (noise). It features both a standalone version and a photoshop plugin. It filters out noise in 48 bit per pixel color space. It makes a profile of your camera that you can apply to a set of photo's. It features both a standalone version and a photoshop plugin. Icecream Image Resizer is a friendly photography software that can resize images without ruining or distorting the quality. The better products allow to create noise profiles in order to provide adjustments to that process.Ī comparison with tests and a list of several tools of the Filter category is found on Michael Almond's page Neat Image Reducing noise or grain in digital images by filter is somehow difficult, since the software has to automatically distinguish between image details and noise. ![]()
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