## Kamis, 25 Maret 2010

### Digital Image Processing

On this occasion, I will tell friends, like where the image-making process that's, of course with a mathematical calculation. This article dibat based on the knowledge I can in college lho. Ok aja deh slim, What is a digital image?

Image (image): can be defined as two-dimensional function f (x, y) where x and y are spatial coordinates and the amplitude of f at any pair (x, y) is called the intensity (gray level) image at that point.

If x and y finite (Finite) and discrete (non continuous) is called a digital image. Digital image consists of a number of finite elements each have a location and value.

The elements x and y is the image elements / pels / pixel. Digital image is the image with f (x, y) whose value didigitalisasi's (made discrete) both in spatial coordinates and in his gray levels. Digitalization of the image spatial coordinates is called the image sampling, while the digitization of the gray-level image is called the gray-level quantization. Digital image can be thought of as a matrix where the rows and columns show the gray level at that point. The elements of the digital image is usually referred to as pixels, which stands for picture elements.

Digital image processing goal is to get a new image that is more suitable for use in certain applications. One type of image processing is called contrast stretching.
Contrast stretching is a technique used to obtain a new image with better contrast than the contrast of the image origin. Image with low contrast can occur due to lack of lighting, lack of dynamism of the image sensor, or an error setting the lens opening at the image capture. The idea of the contrast stretching is to increase the dynamic field of gray levels in the image to be processed.

Basic Color
RGB is a color model that consists of red, green, and blue, are combined in order to form a broad color. Each basic color, red for instance, can be given a range-value. For computer monitors, the smallest range value = 0 and the greatest = 255. Options 256 scale is based on how to uncover 8 digit binary number that is used by computers. In this way, will get a mix of colors 256 x 256 x 256 = 1,677,726 colors. A type of color, can be thought of as a vector in 3-dimensional space is usually used in mathematics, the coordinates are expressed in the form of three numbers, namely x-components, components and component-y-z. Suppose that a vector is written as r = (x, y, z). For color, the components are replaced by the components R (ed), G (reen), B (lue). Thus, a type of color can be written as follows: color = RGB (30, 75, 255). White = RGB (255,255,255), while for black = RGB (0,0,0).

Gray image
Graysacale the pixel colors that are in the range of shades of black and white. For example, if we have a 200x300 pixel size image, then the amount of unused byter in memory is 200x300x2 = 120000bit.

Binary image
Binary image obtained through the separation process based on the pixels that have gray degrees. Pixels that have gray degrees lower than the set limit values will be given the value 0, while pixels that have gray degree greater than the limit value will be converted to 1.
So for a picture which is usually black Puth, his values 0 and 1.
For example, if we have a 200x300 pixel size image, then the unused amount byter in memory is 200x300x1 = 60000bit.

Changing the brightness IMAGE (IMAGE BRIGHTNESS)
Image Brightness (light images) is a technique to make images become brighter or darker. Brightness / kecermelangan images can be done by adding (or subtracting) a constant from each pixel in the image. Image Brightness process causes the histogram of the image changes.
In the process of image brightness mathematically written as:

* F (x, y) '= f (x, y) + b

If b> 0, then the image brightness increases, on the other hand, if b <0 the brightness of the image decreases.

Pixel value of the processing results may be less than equal to the minimum degree of gray (0) or more than equal to the maximum degree of gray (255). Therefore, these pixels need to be clipping to a minimum gray value or the maximum gray value.

Quality improvement of image (image enhancement)
This type of operation aims to improve the image in a way to manipulate the image parameters. With this operation, the special characteristic that there are special is more highlighted in the image.

Operation examples improvement of image:

a. improvement contrast brightness
b. improvement edge object (edge enhancement)
c. Sharpening (Sharpening)
d. Giving false color (pseudocoloring)
e. Filtering noise (noise filtering)

Image restoration (image restoration)
This operation aims to eliminate defects in the image. Image restoration goals similar to the image perbaikkan operation. The difference is, the restoration of the image causes image degradation is known.

Examples of image restoration operations:
a. Disappearance vagueness (deblurring)
b. Elimination of noise (noise)

Image compression (image compression)
This type of operation performed for the image can be represented in a more compact and require less memory. Important things that must be considered in image compression is the image that has been compressed to keep a good image quality.

Image segmentation (segmentation images)
This type of operation aims to break an image into several segments with a certain criteria. This type of operation is closely related to pattern recognition.

Image analysis
This type of operation is aimed to calculate kuantitif amount of images to produce decryption. Image processing techniques extract a particular characteristic that helps in the identification of objects. Segmentation process is sometimes necessary to locate the desired object from its surroundings.
Operation examples pengorakan image:
a. Object edge detection (edge detection)
b. Extraction limit (boundary)
c. Regional representation (region)

Reconstruction image (Image Reconstruction)
This type of operation aims to reshape the image of the object of several projected results. Image reconstruction operations are widely used in the medical field.