What's that noise? Part one: Shedding some light on the sources of noise
How would you react if I told you that the aperture and shutter speed you choose make more difference to image noise than the ISO setting? You might be surprised to discover that a lot of the noise in your images doesn't come from your camera at all: it comes from the light you're capturing.
To really understand what's going on in your camera and, hence, how to get the best results out of it, it helps to understand a little bit about where noise comes from. Noise is widely misunderstood and yet it lies at the heart of most technical assessments of image quality and camera capability.
In simple terms, noise is any variation from the true 'signal' that you're trying to capture. Visually it's immediately recognizable as pixels of unexpected brightness or color, most easily seen in areas where you might otherwise expect a smooth result. The first thing to realize is that there are several mechanisms that contribute towards noise and that all of them can play a different role, even within a single image.
In this multi-part article, we're going to look at two major sources of noise: shot noise and electronic noise, and what they mean for the way you shoot. And because this first source of noise is so significant, we're not going to talk about electronic noise at all in this first part. By the end of both parts you should understand where noise comes from, where the strengths of your camera lie, and hence how to get the best out of it (and when it's likely to let you down).
Probably the most significant and certainly least recognized source of noise is what we call 'shot noise' or 'photon shot noise'. In the simplest terms, this is you being able to see the impact of light's inherent randomness.
You might remember talk of photons from science classes, and the key thing to remember is that, although we perceive light as being pretty uniform, it actually travels as a series of packets. That is: light is quantized. These packets (photons) arrive at your eye or your camera sensor at random intervals. Because of the way the eye and the brain work, you don't notice this, but when you look at a scene, you're being bombarded by little packets of randomly occurring light from every part of that scene.
'So what?' you might say. Well, let's try a little thought experiment. Rain is also a series of discrete packets, that fall randomly over time, so it makes a fairly robust metaphor for the way light behaves. So let's see what happens if we imagine trying to measure rain, using a series of test tubes.
Stand outside in the rain, with your group of test tubes covered with a plastic card. Remove the card for a fraction of a second, then quickly put it back over the tubes. You probably wouldn't be at all surprised to find no raindrops in one tube, two in another and one in the other two tubes because, just like light, raindrops occur randomly. Try the experiment again and leave the tubes exposed for a longer period of time: now you'll find all your test tubes are nearly full. If you tried measuring in detail, you might find that you had 420 drops in one tube, 380 in another and 400 in the other two tubes, but generally, you'd probably look at them and conclude they're all pretty much the same.*1
|Short exposure||Long exposure|
This randomness, even though it's raining similarly hard over all the test tubes, is noise: it's variation from the underlying signal.
An important thing to know is that, although the absolute differences in raindrops collected are larger for the longer exposure (it's a difference of 20 raindrops!), it only accounts for a small proportion of the total number (+/- 5%). With the very short exposure to the rain, the differences were only +/- 1 raindrop between test tubes, but proportionately it made a huge difference (+/- 100%). Or, to use the correct terminology, the signal you captured in the short exposure was small, relative to the amount of noise you experienced: you had much lower signal-to-noise ratio.
This is exactly how shot noise contributes to your photographs. A darker exposure gives you less chance to catch photons, so you're more likely to be able to see the random nature of them hitting your pixels. And this doesn't just apply to bright or dark exposures, it also applies to bright and dark areas within the same image. Bright parts of the image are made up from more photons hitting them during the exposure, so it's harder to perceive any variance between neighboring pixels whereas, in the darker parts of the image, fewer photons hit your sensor, so you're more likely to be able to see the underlying randomness.
The important thing to realize is that this type of noise is present whenever you try to capture light. Whether you use film or digital, medium format or a smartphone, all of the light you're capturing has shot noise built into it. And the solution is always the same: the more light you are able to capture, the less you'll be able to see that noise.
The effect of exposure on noise
Understanding the sources and effects of noise in your final image requires a good understanding of the process of capturing light and the steps it passes through on the way to your final image. The diagrams below show an image of a scene you might want to photograph, with the stripe just below the image showing the scene brightness. The light blue dots within this stripe represent the shot noise that you might experience: there are fewer of them at the dark end of the wedge, but they're more visible.
A key thing to note is that: once captured, the signal-to-noise ratio of any tone can't be improved upon.*2 It can get worse as electronic noise is added, but if you try boosting or pushing the signal, you end up boosting the noise by the same amount and the ratio stays the same. This is why your initial exposure is so important.
The diagram then shows how the exposure you choose maps the brightness of the scene to tones captured by your sensor. Because the image at the top of the diagram represents the scene, its brightness doesn't change even the exposure is changed:
This information captured by the sensor then passes through an amplifier before being converted to digital values and written into the Raw file (in this case we're showing base ISO with minimal amplification, so the brightest tone from the sensor is being mapped to roughly the highest value in the Raw file):
Then, finally, a tone curve is applied, to map this Raw data into a final image. This can be a standard tone curve applied by your camera's JPEG engine or the result of manual Raw conversion and the adjustments made in processing software.
We'll add the contribution from electronic noise in the next article. For now, though, let's look at how noise is affected by exposure:
|Initial Exposure, showing how the highlights, midtones and shadows in the scene are mapped all the way through to the JPEG.
||-1EV Exposure. The camera now captures its highlight, midtone and shadows from brighter parts of the scene.
First, let's look at what happens when you, without making any other changes. The darker exposure captures extra highlight information and you collect each output tone from a brighter point in the scene. Shot noise remains the same if you look at any given brightness level in the final image...
...however, since you captured less light,And, because each part of the scene is made up from less signal, it will have a worse signal-to-noise ratio and so will appear noisier.
This becomes apparent if you try increasing the ISO to match the original image brightness using this reduced exposure...
Combine this reduced exposure with increased ISO sensitivity (so that we get the same final image brightness) and the following happens:
||1EV ISO increase
Just as in the example above, the reduced exposure means any point in the scene appears darker on the sensor (you've captured less signal at every point in the scene). However, the camera is then applying additional amplification to make up for this, pulling the brightness back up again. And, as mentioned earlier, these amplified tones maintain their signal-to-noise ratios, which we've already seen are lower, compared to our original exposure. This result should be immediately familiar: turn the ISO up on your camera and you get the same image brightness from less exposure but at the cost of noisier images. Electronic noise can play a role in this, but the key lesson from this article is that a lot of the noise you see isn't coming from your sensor or your camera: it's inherent in the randomness of the light you captured and it's primarily dependent on the exposure you chose.
Take a look atand it explains a lot of why your high-ISO images are noisy: most of your image is being made up from the shadow regions of your Raw file (that was captured with poor signal-to-noise ratios), amplified to be brighter.
Another interesting thing to note is that, at least initially,. However, increasing the amplification meant they no longer fit in the Raw file, so they 'clipped' to white. This is also why higher ISO images have increasingly little dynamic range: you're amplifying-away a large chunk of the information your sensor captured. This will become relevant when we discuss some camera's dynamic range modes and when we discuss our ISO Invariance test, in the next article.
The effect of sensor size
There are three factors that affect how much light is available for your sensor to capture: your shutter speed, f-number and the size of your sensor.
As we showed in our equivalence article, a full frame camera shot at 85mm F5.6 and a Four Thirds camera at 42.5mm F2.8 will have the same angle of view and the same aperture size (15.2mm diameter) and hence will be exposed to the same amount of light if exposed for the same amount of time. Or, put another way, at the same f-number (both cameras set to F2.8), the full frame camera will see four times as much light as a camera with a Four Thirds sensor, since it is exposed to the same light-per-unit-area but has a sensor with four times the area.
As a result, when you shoot two different sized sensors with the same shutter speed, f-number and ISO, the camera with the smaller sensor has to produce the same final image brightness (which the ISO standard demands) from less total light. And, since we've established that capturing more light improves your signal-to-noise ratio, this means every output tone from the larger sensor will have a better signal-to-noise ratio, so will look cleaner. Click here if you want to see an example.
On a bright day, you're unlikely to notice any significant difference in the highlights and midtones since, even on very small sensors, you're usually capturing enough light to give a good signal-to-noise ratio such that the noise isn't particularly visible. However, in shadow regions, which are made up of less and less signal and, therefore, lower and lower signal-to-noise ratio, the differences will become apparent. And in lower light, an increasing part of the image will be made from tones captured with low signal-to-noise ratios and the noise will start to become obvious. This is the reason smaller sensor cameras tend to produce noisier images.
In turn, this is why we talk about different sensor sizes representing an image quality/size/price balance: because, so long as the sensor's electronic performance is similar, the effect of shot noise means that sensor size is the major determinant of image quality. Yes, pixel count can make some difference, but shot noise tends to play a much bigger role, if you compare images at the same output size.
How does this affect my photography? Exposing to the right
Whatever your sensor size, the most effective way of achieving the best results is to 'expose to the right.' It's a phrase you may have seen used, either in full or as the abbreviation ETTR. In the most simple terms, it means using the brightest exposure you can possibly get away with, without information clipping to white (at which point it's impossible to recover with any color accuracy). The name comes from the appearance of the camera's histogram (the graph showing the distribution of image brightness in the image), where the right-hand side represents bright tones: you're trying to expose so your image data is as far to the right as possible without too much clipping.
This technique only really works with Raw files, since you're setting your exposure based on the brightest point in the scene, not on whether anything in the image is the 'correct' brightness. This means that you'll have to process the Raw file afterwards to adjust the image brightness the way you want it.
The other downside is that, because your camera's exposure meter and exposure guides are all based around putting scene midtones at the correct JPEG brightness level, they're not as helpful as they could be for assessing when you your Raw file has been correctly exposed to the right.
However, the benefit of this approach is that you get your sensor to capture as much light as possible without important tones clipping. This ensures that every tone you want to capture in the scene is made up of as much signal, and the least amount of shot noise contribution, as possible, and hence has the best possible signal-to-noise-ratio. This will get you the best possible noise performance and the most flexible Raw file.*3
Of course there will be situations in which there isn't enough light to expose all the way to the right (or your subject is moving so fast that you need a really fast shutter speed). In this situation you have two choices: to raise the amplification or to under-expose a lower ISO and push the Raw file later. The option you choose should depend on the specific behavior of your camera's sensor, and that's something we'll look at in more detail in part 2.
*1: The figures we've used in this example are deliberate: shot noise contributes a variation of +/- the square root of the signal. [Jump back to text]
*2: Technically speaking, you can improve the signal-to-noise ratio with noise reduction, which attempts to remove noise while preserving the signal. However, this typically comes at the cost of detail retention. [Jump back to text]
*3: There's a misconception that you ETTR so as to make use of 'more available levels' at the higher end of the Raw file, since the greater number of levels there should record finer tonal transitions. The reality, though, is that shot noise dithers these finer transition, so all those 'extra bits' at the upper end of the Raw file go towards just more finely representing noise. Some of the smarter Raw compression systems take advantage of this principle to more efficiently encode a wide dynamic range into a lower bit-depth Raw file in a visually lossless manner.
Acknowledgements: Thanks to Rishi Sanyal for the extensive input into this article and the many discussions it builds upon, and to Simon Joinson for prettifying the tone flow diagrams.
|Waffles with fruits by Coolinarka|
from Food photography (desserts)
|Vestrahorn Frozen Reflection by Will B Milner|
from Ice cold
It's nicknamed the 'Cycloptic Mustard Monster,' and is a 3D printed medium format camera. Read more
An honest defense of the system's merits, with photos as proof.
We’ve all seen Bob Jackson’s Pulitzer Prize winning photo, but there's another.
The sample footage looks good.
Make those old photos disappear without deleting them forever.