49843608-Complete-thesis-Report-merged

# 28 spatial domain methods the value of a pixel with

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2.8 Spatial Domain methods The value of a pixel with coordinates (x, y) in the enhanced image is the result of performing some operation on the pixels in the neighborhood of (x, y) in the input image, F. Neighborhoods can be any shape, but usually they are rectangular [3]. Figure 2.7 Spatial domain of an image.

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22 Image Processing in the spatial domain can be Expressed by: g (m, n) =T (f (m, n)). Where f (m, n) is the input image, g (m, n) is the processed Image and T is the operator defining the modification process. The operator ‗T‘ is typically a single -valued and monotone function that can operate on individual pixels or on selective value of the input Image is used to compute the corresponding pixel value for the output Image. Within the input image are used to compute the modified image at any given point. One can consider point processing as a special case of region processing where the region is composed of a single pixel. The point-processing operator can also be expressed by: S=T(r), Where r and s are variables denoting the intensity level of f( m , n) and g( m ,n) at any point ( m ,n). The following sections describe several point-and region-based image enhancement techniques. The second article will also discuss spatial smoothing, which is another example of region-based image processing, and compare its advantages and dies- advantages with transform domain methods. The smallest possible neighborhood is of size 1 X 1. In this case g depends only on the value of f at a single point (x, y) and T [3]. 2.8.1 Creating Negative of an Image The most basic and simple operation in digital image processing is to compute the negative of an image. The pixel gray values are inverted to compute the negative of an image. For example, if an image of size R x C, where R represents number of rows and C represents number of columns, is represented by me (r, c). The negative N(r, c) of image I(r, c) can be computed as N(r, c) = 255- I(r, c) where 0≤ r ≥R and 0≤ c ≤C. It can be seen that every pixel value from the original image is subtracted from the 255. The resultant image becomes negative of the original image. Negative images are useful for enhancing white or grey detail embedded in dark regions of an image [19].
23 Figure 2.8 Creating Negative of an image [19]. 2.8.2 Intensity Transformation Intensity transformations are among the simplest of all image processing techniques. The value of pixels, before and after processing, will be denoted by r and s, respectively. An intensity transformation function of the form s=T(r) [18]. Where T is a transformation that maps a pixel value r into a pixel value s. because we are dealing with digital quantities, values of a transformation function typically are stored in a one-dimensional array and the mappings from r to s are implemented via table lookups. For an 8-bit environment, a lookup table containing the values of T will have 256 entries [19].

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