You have probably already seen digital noise as the colored speckles or grains over images particularly those taken at night.
Noise is any kind of deviation from what’s expected at the pixel level. For example, your camera may record something slightly darker or lighter. When this happens on a significant enough scale to end up with a visible texture, which refers to noise.
It’s usually easier to see noise in flat featureless areas of an image such as in a sky rather than in scenes of more intricate details. The reason for this is the busier scenes effectively hide some of the noise whereas areas of little detail could be seen with some noise if you look closely.
Causes of noise in an image
There are number of reasons why images look distorted and grainy. Some of which you can’t do anything about. Some noise for example happens because of how light arrives at the sensor and some is generated simply from converting each pixel into digital information. Let’s see more about what causes digital noise in images.
- ISO
ISO is the main cause of noise. Higher ISO that should be used in low light or at night distorts the image when clicked in daylight. So, higher the ISO, more noise will exist in the image.
- Sensor Size
For noise, the size of the sensor counts. Mobile phone cameras and compact cameras have a small sensor to their camera. So, when we click on these devices it can’t even reach the level of 400 ISO that makes the image more distorted and color fidelity can be noticed easily. Whereas when taken on larger cameras like DSLRs, the produced image is much clearer and detailed, as the lower grain image is produced by them.
- Pixel Density
This can be explained by taking an example. Suppose a camera with sensor of 14 MP clicks an image. Now as we know, that it will generate more noise than a lower megapixel camera. Why?
Because when we click a picture from a 14 MP camera, it will squeeze the actual pixels as compared to low-MP camera. This will automatically distort the image.
- Exposure Time
Longer exposure time can also create the noise.
Now, let’s see what types of noise could be caused.
Types of noise in image processing
- Gaussian Noise Model or Amplifier Noise
Gaussian Noise refers to the statistical noise with PDF (probability distribution function) equal to normal distribution. It is also known as Gaussian Distribution.
In terms of image processing, gaussian noise is additive in nature and has zero mean value.
This type of noise can be filtered using spatial filter that smoothens the image. Gaussian smoothing, also known as gaussian blur is produced by the Gaussian function in an image. This smoothing is used in graphics and designing software to reduce image noise and details.
- Salt and Pepper Noise
Salt and pepper noise is also known as shot noise or Impulse noise. This noise is caused by disturbance during the image taken. Malfunctioning of camera sensors or timing error can cause this type of noise in an image.
Basically, when we take a look at an image, it shows up as black and white pixels on an image, that is why it is called salt and pepper noise.
This type of noise can be filtered by using mean and median filter technique or by gaussian filter also.
- Quantization Noise
Quantization noise is also known as uniform noise. This noise is caused by quantization of pixels of an image up to number of levels. It is called uniform noise because its distribution is approximately uniform throughout the process.
It can be used to produce various types of noise distribution as it has uniform distribution as well as for degrading the images so that it can be used in image restoration algorithms.
How can noise be avoided in images?
Sensors also produce more noise in warmer conditions and as they heat up through use. So, you should bear this in mind when shooting in particularly hot conditions.
There are number of things to minimize noise appearing in your images. The general rule is using the lower ISO settings available will lead to less noise forming. So only use the high one when you must.
If your camera has an ISO setting it may allow you to set maximum sensitivity so that you can avoid using sensitivities past a certain point.
Instead of using the high sensitivity, you could try to use a wide aperture this will let lighter pass through the sensor and will stand to create a cleaner image than the high ISO setting. Although you should make sure that the wide aperture maintains the depth of field that you want in your image. You could also try a long exposure instead of high ISO settings.
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