Image Enhancement - Point Processing

Digital Image Processing - Image Enhancement - Intensity Transformation Functions - Point Processing - Point Operations - Gray-level Mapping

1. Point Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Presented By, J. Steffi T. Navis R. Gayathr i Manonmaniam Sundaranar University Tirunelveli - 12 M.Phil. Computer Science 2017

2. Digital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An image is a spatial representation of a two dimensional or three dimensional scene . A digital image is composed of a finite number of elements, each of which has a particular location and value . A digital image are classified into three types, Black/White or Binary image Gray-scale image Color/RGB image

3. Digital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A digital image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The digital image processing methods used in various areas, such as : Medical imaging Astronomy Remote Earth resource observation

4. Digital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The basic digital image processing methods are , Image Enhancement Image Restoration Image Compression Image Segmentation

5. Image enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Image Enhancement is the process of manipulating or adjusting digital image so that the result is more suitable for display or further image analysis. For example , you can remove noise, sharpen, smooth or brighten an image, make it easier to identify key features.

6. Spatial domain . . . . . . . . . . . . . . . . . . . . . . . . . Image enhancement methods operate in, S patial domain - manipulating the pixel data Frequency domain - modifying the spectral component Spatial domain refers to the image plane itself. Image processing methods in this category are based on direct manipulation of pixels in an image.

7. Neighbours of a pixel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (x, y) 3 x 3 neighborhood of (x, y) Image f Spatial domain pixel (x, y) 4-neighbors of pixel (x, y) 8 -neighbors of pixel (x, y) &

8. Spatial domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A 3 x 3 neighborhood about a point ( x, y ) in an image in the spatial domain. The neighborhood is moved from pixel to pixel in the image to generate an output image. (x, y) 3 x 3 neighborhood of (x, y) Image f Spatial domain

9. Spatial domain processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two principal categories of spatial domain processing are Intensity Transformation Spatial Filtering Intensity t ransformation operate on single pixels of an image, principally for the purpose of contrast manipulation and image threshold. Spatial filtering deals with performing operations, such as image sharpening, by working in a neighborhood of every pixel in an image.

10. Spatial domain process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The spatial domain process can be denoted by the expression, g ( x, y ) = T [ f ( x, y ) ] Here, x and y are spatial ( plane ) coordinates ( x, y ) is the intensity/gray level the image at that point f ( x, y ) is the input image g ( x, y ) is the output image T is an operator

11. Point processing . . . . . . . . . . . . . . . . . . . . . . . . . . . The point processing is a simple method of image enhancement. The point processing technique, which results depend only on the intensity at a point. This point processing function of the form s = T ( r ) Here, s refers to the processed image r refers to the original image T is a transformation that maps a pixel value r into a pixel value s

12. Point processing functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The point processing function is also called as intensity transformation or gray - level mapping function. The point processing functions can be further categorized into two types. They are as follow : Linear Functions Non – Linear Functions

13. Point processing functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear intensity transformation functions The negative transformation functions The identity transformation functions Non – linear transformation functions The Logarithmic log transformation functions inverse log transformation functions The Power – l aw n th power transformation functions n th root transformation functions

14. Point processing functions . . . . . . . . . . . . . . . . . . . . . . . . . Basic intensity transformation functions

15. image negatives . . . . . . . . . . . . . . . . . . . . . . . . . The negative of an image with intensity levels in the range [ 0, L-1] is obtained by using the negative transformation. The negative transformation is given by the following expression, s = ( L - 1 ) – r s is a negative image L has a maximum gray level range of values 0 to 255 r is an original gray-scale image Reversing the intensity levels of an image in this manner produces the equivalent of photographic negative. This type of processing is particularly suited for enhancing white or gray detail embedded in dark regions of an image.

16. image negatives . . . . . . . . . . . . . . . . . . . . . . . . . Gray – Scale Image Negative Image Example for image negative transformation

17. Log transformation . . . . . . . . . . . . . . . . . . . . . . . . . The general form of the log transformation is, s = c log ( 1 + r ) where, c is constant, and it is assumed that r >= 0. The log transformation maps a narrow range of low input gray level values into a wider range of output values. The inverse log transformation performs the opposite transformation.

18. Log transformation . . . . . . . . . . . . . . . . . . . . . . . . . The shape of the log curve in image shows that the log transformation,

19. Log transformation . . . . . . . . . . . . . . . . . . . . . . . . . Log functions are particularly useful when the input gray level values may have an extremely large range of values. The following example shows the result of applying the log transform into a gray-scale image is used to reveal more detail.

20. Log transformation . . . . . . . . . . . . . . . . . . . . . . . . . Gray – Scale Image Result of applying the log transformation with c = 1 Example for log transformation

21. Power-Law transformation . . . . . . . . . . . . . . . . . . . . . . . . . The power-law transformation is also called as gamma transformation. The power-law transformation have the following form s = c r γ where c and γ are positive constants and plots of s versus r for various values of γ . In power-law transformation, power-law curves with fractional values of γ map a narrow range of dark input values into a wide range of output values .

22. Power-Law transformation . . . . . . . . . . . . . . . . . . . . . . . . . Plots of the equation s = cr γ for various values of γ (c = 1 in all cases)

23. Example for power-law transformation Power-Law transformation . . . . . . . . . . . . . . . . . . . . . . . . . Aerial Image Gam m a Corrected Image with γ = 5.

24. Thank you . . . . . . . . . . . . . . .