Essay: Fundamentals of Digital image processing

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  • Fundamentals of Digital image processing
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Image is most important thing in digital image processing. An image is a visual representation of a thing, person etc are display by an optical device such as mirrors a lenses or camera [1].
An image are two dimensional function T (p, q), where x and y are spatial (plane) coordinates (p, q). This coordinates are called gray level of the image. T, p and q are all finite and discrete quantities, the image is called a digital image [1].
Following figure show the gray level image. An image is used in many fields for example entertainment, remote sensing, medical image, etc. which is very useful for human life. In this thesis gray scale image is use for remove impulse noise.
The human are received major portion of information from the environment in visual. The receiving visual data by the human in computer this process is called ‘digital image processing’. Digital images are used on two tasks:
‘ Images data or information for transmission and storing information.
‘ Implementation of pictorial information for human interpretation.[1].
The main purpose of digital image processing is to improve information for person interpretation and then process the image data for storing, transmission etc for autonomous devices. Digital image processing is many area of research in the fields of computer aided manufacturing (CAM), biomedical instrumentation, electronics, robotics, consumer and entertainment electronics, control and instrumentation and communication engineering etc. important processing such as image restoration and image enhancement[2].
Digital image processing is improving or editing digital images using a personal device or computer. Digital image technique and applications usually take an image as input and produced output. These outputs are a modified image and encoded image etc [3]. Image processing refers to a set of procedures which aims at modifying the appearance and nature of an image is either enhance its pictorial information content for user interpretation or make it suitable enough for developing applications and autonomous machine perception.
In digital image processing digital image is developed in system. That performs many operations in image. A Digital images is composed of a finite set of digital values are called pixels values or picture elements. The picture elements and pixel values are also called pixels and pels. Pixel is mostly used to represent the element of digital image [1].
In figure 1.2 shows the image by two dimensional function of the form f(x, y). The amplitude of f at spatial coordinates (x,y) in a source of the image. Image is generated by physical sources such as electromagnetic waves. For consequence f(x, y) must be non zero and finite that is
0 < f(x, y) < ' 1.2.1 Types of digital image 1. Gray-scale image 2. Binary image 3. Color image 1.2.1.1 Gray-scale image Gray scale image are representation of two dimensional arrays. In gray image 8-bits per pixels are included. Where a '0' denote 'black' dot and a '255' denote 'white' dot. It is a data matrix and its values are identifying shades of gray. In MATLAB programming the elements of a gray scale images are of class U int 8 or U int 16, they have integer values in the range [0,255] or [0,65535], respectively. Image of class double or single are in float value. 1.2.1.2 Binary image Binary image are representation of two dimensional arrays. In binary image 1 bit per pixel are required. Where '0' denote 'black' and a'1' denote 'white'. It is used on simple graphics, line art or text in its small size. In MATLAB programming binary image are represent logical array of 0's and 1's and an array of 0's and 1's whose values of data class say U int 8 is not considered it in MATLAB[4]. 1.2.1.3 Color image In color image each pixel value has a specific color that color denoted by the amount of Red (R) Green (G) and Blue (B) and indexed color image. Color images are two types, RGB color image and indexed color image. 1.2.1.3.1 RGB color image RGB color image is used 24 bits and it is represented using three channels for 2 D arrays of same size, and one channel for each color red, green and blue. Each array element contains an 8-bit value, indicating the amount of RGB at that point in a [0,255] scale and the combination of the three 8-bit values into a 24-bit number allows 2^24 (16,777,216 or 16 million) color combinations. 1.2.1.3.2 Indexed color images Indexed color image is solving the problem of 24 bits color images. In 24-bit color representation is backward sympathy with hardware that cannot be able to display, the 16 million colors. In indexed color image indexed representation a two dimensional array of the same size as the image contains indices (pointer) to a color palette (or color map) of fixed maximum size (256 colors) [4]. Digital image processing has input and output is images and remaining three functions have input images and output are attributes of input. There are many functions of digital image processing. 1. Acquisition 2. Restoration 3. Image enhancement 4. Morphological processing 5. Color image processing 6. Image compression 7. Segmentation 8. Object recognition 9. Image Representation & Description There are starting six functions images. This thesis use image enhancement and image restoration function for digital image processing. Image enhancement have important function is that to improve quality and manipulating of image which is more effective for increasing the contrast and changing the brightness level of image [5]. When an image is processed for visual interpretation the viewer is judge working of particular method [1]. In Enhancement the image has on the interpreter in a fashion that improves the information content. 1. Contrast enhancement 2. Hue and saturation transformations 3. Density slicing 4. Edge enhancement 5. Making digital mosaics 6. Producing synthetic stereo images In contrast enhancement technique is also in three forms such as contrast enhancement, linear contrast, and non linear contrast. In contrast enhancement technique has strong influence of contrast ratio on resolving power and detection capability of images. The simplest linear contrast enhancement is called a linear contrast stretch. The image processing software displays an image after linear stretching. The non linear contrast is different technique .in non linear contrast applied the greatest contrast enhancement to the most populated range or brightness values in the original image. Hue and saturation transformation is used in different colors RGB color images for enhancement. Density slicing is used to converts continuous grey tone of an image into a series of density intervals or each are display they are abounded by contour lines. Edge enhancement is used to modify image edge. Edge enhancement is two type directional and non directional filters. Mosaics of images are prepared by matching and splicing together for individual images [6]. Image Restoration technique is used to recover original scene from the degraded image and observations image restoration is used to remove noise, data error etc and improve the quality of image and information of data [7]. This technique is concerned with the reconstruction or estimation of the uncorrupted image from a noisy and blurred. Image restoration is objective in the senses that restoration techniques are based on mathematical or probabilistic model of image degradation. Image restoration and image enhancement have important difference is that image enhancement is subjective process and image restoration is objective process[8].Image restoration method is distinguished from image enhancement methods because it is based on the model of the degrading process and unique image [9]. Image restoration for data errors, noise and geometric distortions introduced during the scanning, recording and playback operations [6]. 1. Filtering of random noise 2. Restoring periodic line drop outs. 3. Restoring periodic line striping 4. Correcting for atmospheric scattering 5. Correcting geometric distortions Restoration technique block diagram is show in following figure 1.4. In figure show the process of restoration technique. In this technique input image or true image F (x,y) , before transmission noise H (x,y) are add in image then this image is noisy image g (x,y) are remove noise use of different restoration filters and get output image or restored image. So we find noise free image with the help of restoration techniques. Restoration technique is used modeling of degradation and applying inverse process for recover image [8].

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