![]() Other than obtaining improved electronics, the best way to defeat read noise is to ensure that you expose long enough during a single exposure that the faintest feature you want to record has signal well above the read noise. Unlike other noise sources, read noise is independent of exposure time, meaning you pay a fixed price for read noise for every frame, regardless of whether it’s a 2-second exposure or a 20-minute exposure. Read noise has the most impact on faint signals. As each pixel value is being read out, a few extra electrons are lost or gained randomly, causing the readout value to vary a little from the actual captured signal rate. This noise or uncertainty is due to your camera's electronics. Stay with me for what dark frames do… Read Noise To reduce this noise source, you need to stack many frames and/or cool your camera. Contrary to many sources on the web, subtracting a dark frame doesn't wholly remove this noise source, it only removes the offset. Regardless of what you call it, it’s noise, and we don’t want it. I’ve seen this noise source referred to as well as dark shot noise, or thermal noise. But it's not a simple offset - the dark current has the same kind of random fluctuations as shot noise. This adds an offset to the signal for every exposure. Even with no light shining on it, an image sensor will accumulate signal from the thermal energy (i.e., heat) in the chip itself. The reason we cool our cameras is to control this noise source. Dark Current Noise Uncooled (top) has FAR more noise from the dark current than the cooled (bottom) image. Our primary weapon for this kind of noise is to take longer exposures and stack images. The more light you have, the more quickly you’ll obtain a nice, smooth, uniform image. With just a little light, you end up with a very uneven, splattered look - In the vernacular of the peasantry, we might call this appearance “grainy” - until the detector slowly builds up enough signal. Light falling on your image sensor behaves pretty much just like this. A huge rain shower, on the other hand, will douse the sidewalk immediately. There’s a splat here and a plop there, leaving a mottled look until enough rain drops have fallen to make the sidewalk uniformly wet. Think of a few sprinkles of rain hitting the sidewalk. This is the random fluctuation in the arrival of photons as your sensor captures them. For now, I'll lay a foundation before addressing these sources one by one in more detailed future installments. There are a few well-known sources of noise that we care most about, and we have various processing techniques to combat them. and yes, they can be represented by numbers! The higher/bigger/larger the S/N, the higher the quality of your data. What we strive to optimize is the signal to noise ratio, or S/N (signal divided by noise). Many of the techniques I’ll discuss in this space in the future are about optimizing signal and reducing noise. We want lots of signal, and we want as little noise as possible. Essentially, noise is unwanted junk and signal is the pretty stuff you are trying to capture. Noise and signal are possibly the two most important concepts to understand when it comes to astrophotography. You don't need a PhD to understand noise in astronomical images - here's an introduction to the various sources of noise in astrophotography and how to combat them. ![]()
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