An image representation is worth more than ten thousand words. A digital image is an image with spatial co-ordinates and amplitude values are finite in discrete quantities. The field of digital image processing refers to processing of two dimensional pictures by a digital computer.
Digital image processing has a broad spectrum of applications, such as robotics, biology, industry, astronomy, law enforcement, defense and automated inspection of industrial parts.
The area of applications of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. The simplest way to develop a basic understanding level of image processing applications is to classify the images according to their source.
1.1. Fundamental steps in Digital Image processing
The fundamental steps in digital image processing are acquisition, enhancement, restoration, compression, segmentation, representation and object recognition.
In Image acquisition, our objective is to generate digital images from sensed data. The output of most sensors is a continuous voltage waveform whose amplitude and spatial behavior are related to the physical phenomenon being sensed. To create a digital image we need to convert continuous sensed data into digital form.
Image enhancement is processing of manipulating an image so that the result is more suitable than the original for a particular application. Enhancement techniques are not suitable for all applications. For example, a technique that is helpful for improving X-ray images may not be the best technique for enhancing satellite images taken in the infrared band of the electromagnetic spectrum.
Fig. Fundamental Steps in Digital Image Processing
Image restoration is the process of removal or minimization of known degradations in an image. Random-noise reduction is carried out in the spatial domain using convolution mask and for modeling some important degradation, such as blur caused by motion during acquisition. A restoration filter that solves a given application in the frequency domain often is used as the basis for generating a digital filter that will be more suitable for routine operation using a hardware implementation.
Image compression deals with the techniques for reducing the amount of data required to represent an image. Image compression acts an important role in many areas. Because of the wide applications, data compression is of the great importance in digital image processing.
Image segmentation is a process that partitions an image into regions. Image segmentation is an essential preliminary step in most automatic pictorial pattern recognition and scene analysis application.
Noise in digital image
Noise removal plays an important role in digital image processing. The major source of noise in digital image occurs during image acquisition and transmission. The acquisition noise is generally additive white Gaussian noise
(AWGN) with very low variance and may be quite negligible for many engineering applications. It is mostly due to very high quality sensors. But in some applications such as remote sensing, biomedical instrumentation, etc., the acquisition noise may be high enough. This is due to the fact that the image acquisition system itself comprises of a transmission channel and such noise problems are measured as transmission noise. Then it may be concluded that acquisition noise is insignificant. Therefore, the researchers are mainly concerned with the noise in a transmission system. This noise appearance affects the original information of digital images. Image distortion is an important problem in image processing. Image distorted due to several kind of noise such as salt and pepper noise, Rayleigh noise, speckle noise and many other fundamental noise types in digital images. These noises may be arrived from a noise source established due to imperfection in the image capturing devices, weak focal length, scattering and other undesirable conditions may be appear in the atmosphere. This leads to in-depth study of noise and noise models for image denoising systems.
Image is an important source of information. But the noise in digital images creates undesirable effects such as blurred objects, distorts background views, shadows, unrealistic edges, corners and unseen lines. To reduce these effects, prior knowledge of noise models is necessary for further processing.
Types of noise models
Gaussian noise occurs in amplifiers or detectors. So it is also known as electronic noise. The probability density function of a Gaussian random variable z, is
Where, z represents the gray value, Âµ is the mean value of z, Ïƒ is its standard deviation.
The noise that occurs in coherent imaging of objects is known as speckle noise. Speckle noise is multiplicative noise, having granular pattern. Most of ultrasound images are corrupted by speckle noise.
It is expressed as:
g(m, n) = f(m, n) u(m, n) + Î·(m, n)
where, g(m, n) is corrupted image, u(m, n) is multiplicative component and Î·(m, n) is additive component.
The probability density function of Rayleigh noise is given by
The mean and variance of this density are given by
The Rayleigh density is useful in differentiating noise occurrence in range imaging.
Impulse valued noise
Impulse valued noise takes place in locations where a high transient (faulty switching) arises. The noisy pixels value corrupted by impulse noise are distributed in the range of [0, 255] for grayscale images. Impulse valued noise is also called as salt and pepper noise.
Though the image is not fully degraded by salt and pepper noise as choice of several pixel values are changed in the image. Although in noisy image, there is a possibilities of more than a few neighbors does not changed. Statistically the impulse valued noise drop the original data values. So data-drop-out is also a term used to refer to the impulse noise. The probability density function of impulse noise is given by
If b>a, intensity â€˜bâ€™ will shows a light dot in the image. Conversely, level of â€˜aâ€™ will appear like a dark dot. If either pa or pb is zero, the impulse noise is unipolar. Especially if the probabilities are equal and neither probability is zero, impulse noise values look like salt and pepper granules randomly spread over the image. Eliminating or reducing impulse noise is a very dynamic research area in image processing.
Fig. Probability Density Functions of Impulse Noise
Noise Models for Color Images
There are two types of noise in color images such as correlated color noise and uncorrelated color noise. In second type, noise affects the R, G and B planes separately with the given percentage. In first type, the occurrence of noise in a component of color pixel also depends on its occurrence in other components. It is caused in two steps. In the first step, noise is added in the similar way as uncorrelated noise. In the second step, for every noise-free component in any plane, it is checkered if other two parallel components in other planes are corrupt, and if so, noise-free component is made noisy based on the correlation factor.
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