Image noise
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Image noise corresponds to visible grain or particles present in the image. In the context of digital image processing, the term noise usually refers to the high frequency random perturbations of color values of size close to 1 pixel, which are generally caused by the electronic noise in the input device sensor and circuitry (e.g. scanner, digital camera). There are other artifacts of similar appearance which are referred to with different terms to underline their origin (e.g. scanner streaks, film grain).
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[edit] Types of image noise
- In salt-and-pepper noise (also known as random noise or independent noise), pixels in the image are vastly different in color from their surrounding pixels. The defining characteristic is that the color of a noisy pixel bears no relation to the color of surrounding pixels. Generally this type of noise will only affect a small number of image pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust on the lens or inside the camera, or with digital cameras, faulty CCD elements.
- In Gaussian noise (dependent noise), an amount of noise is added to every part of the picture. Each pixel in the image will be changed from its original value by a (usually) small amount. Taking a plot of the amount of distortion of a pixel against the frequency with which it occurs produces a Gaussian distribution of noise.
[edit] Useful noise
There is a common misconception to consider noise present in the image as an evil to be avoided at any cost. While high levels of noise may be undesirable, there are cases when lower levels of noise turn to be useful, or even indispensable. For instance image noise prevents discretization artifacts (color banding). It can help to integrate composited images, or remove/reduce moire.
[edit] Noise problems with digital cameras
In low light, or at high film speed ("ASA") settings, many digital cameras produce unacceptable image noise. The two examples show the typical difference (best seen in full-size) between a well-lit subject and one in less light. Image noise can also be very prominent in flash photos, which tend to be underexposed if the camera-to-subject distance is large.
[edit] Image noise removal
[edit] Gaussian filters
One method to remove noise is by convolving the original image with a mask. The Gaussian mask comprises elements determined by a Gaussian function. It gives the image a blurred appearance if the standard deviation of the mask is high, and has the effect of smearing out the value of a single pixel over an area of the image. This brings the value of each pixel into closer harmony with the value of its neighbours. Gaussian filtering works relatively well, but the blurring of edges can cause problems, particularly if the output is being fed into edge detection algorithms for computer vision applications.
Averaging is a degenerate case of Gaussian filtering, where the function defining the mask values has an infinite standard deviation.
[edit] Non-Linear filters
A median filter is an example of a non-linear filter and, if properly designed, is very good at preserving image detail. To run a median filter:
- consider each pixel in the image
- sort the neighbouring pixels into order based upon their intensities
- replace the original value of the pixel with the median value from the list
This type of filter is very good at removing salt and pepper noise from an image, and also causes very little blurring of edges, and hence is often used in computer vision applications.
[edit] See also
[edit] External links
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