Digital Signal Processing Reference
In-Depth Information
developed for monochrome images have to be extended in a way that exploits
correlation among the image channels.
The acquisition or transmission of digital images through sensors or com-
munication channels is often inferred by mixed impulsive and Gaussian noise.
In many applications it is indispensable to remove the corrupted pixels to
facilitate subsequent image processing operations, such as edge detection,
image segmentation, and pattern recognition.
Numerous filtering techniques have been proposed to date for color image
processing. Nonlinear filters applied to color images are required to preserve
edges and image details and to remove different kinds of noise. Edge informa-
tion is especially important for human perception. Therefore, its preservation
and possible enhancement are very important subjective features of the per-
formance of nonlinear image filters.
12.1.1
Noise in Color Images
Noise introduces random variations into sensor readings, making them dif-
ferent from the real values, and thus introducing errors and undesirable side
effects in subsequent stages of image processing. Faulty sensors, optic im-
perfectness, electronics interference, data transmission errors, or aging of the
storage material may introduce noise to digital images. In considering the
signal-to-noise ratio (SNR) over practical communication media, such as mi-
crowave or satellite links, there can be degradation in quality, due to low
power of the received signal. Image quality degradation can be also a result
of processing techniques, such as de-mosaicking or aperture correction, that
introduce various noise-like artifacts.
The noise encountered in digital image processing applications cannot al-
ways be described by the commonly assumed Gaussian model. Very often it
has to be characterized in terms of impulsive sequences, which occur in the
form of short-duration, high-energy spikes attaining large amplitudes with
probability higher than predicted by the Gaussian density model. Thus, im-
age filters should be robust to impulsive or generally heavy-tailed noise. In
addition, when color images are processed, care must be taken to preserve
image chromaticity, edges, and fine image structures.
12.1.1.1 Impulsive Noise Models
In many practical applications, images are corrupted by noise caused either
by faulty image sensors or by transmission corruption resulting from anthro-
pogenic phenomena, such as ignition transients in the vicinity of the receivers
or even natural phenomena such as lightning in the atmosphere.
Transmission noise, also known as
salt and pepper
noise in gray-scale imag-
ing, is modeled by an impulsive distribution. However, one of the problems
encountered in the research of noise effects on image quality is the lack of
commonly accepted multivariate impulsive noise models.
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