Full Color Image Processing-Colors Pictures And Digital Image Processing-Lecture Slides, Slides for Digital Image Processing. B R Ambedkar National Institute of Technology
shalaby_88cop
shalaby_88cop20 July 2012

Full Color Image Processing-Colors Pictures And Digital Image Processing-Lecture Slides, Slides for Digital Image Processing. B R Ambedkar National Institute of Technology

PDF (5 MB)
26 pages
3Number of download
1000+Number of visits
Description
Dr. Chittaranjan Verma delivered this lecture for Digital Image Processing course at B R Ambedkar National Institute of Technology. It includes: Full, Image, Processing, Digital, Image, Components, Represented, Function,...
20points
Download points needed to download
this document
Download the document
Preview3 pages / 26
This is only a preview
3 shown on 26 pages
Download the document
This is only a preview
3 shown on 26 pages
Download the document
This is only a preview
3 shown on 26 pages
Download the document
This is only a preview
3 shown on 26 pages
Download the document

2

Full color image processing

Color image mathematical representation Let c represent an arbitrary color in the RGB color space, the color components can be represented as a function of spatial coordinates (x,y)

( , ) ( , ) ( , ) ( , ) ( , )

( , )( , )

for 0 1, 0 1

R

G

B

c x y R x y x y c x y G x y

B x yc x y

x M y N

                 

     

c

docsity.com

3

Full color image processing  Two major categories of full-color image processing

approaches  Per-color-component processing

Process each component image individually form a composite processed color image

 Vector-based processing Work with color pixels (as a vector) directly

 To make results of two approaches equivalent:  The image processing method has to be applicable to both

vectors and scalars  The operation on each component of a vector must be

independent of the other components

docsity.com

4

Full color image processing: Basics

 For example: For neighborhood averaging filtering the intensity of the resultant image is same for per-color- component processing and vector-based processing

docsity.com

5

Color image and its components: RGB

docsity.com

6

Color image and its components: CMYK

docsity.com

7

Color image and its components: HSI

docsity.com

8

Color transformations  Process the components of a color image

within the context of a single color model  Formulation is similar to the gray-level

transformation: g(x,y)=T[f(x,y)]

 f(x,y): color input image  g(x,y):transformed (processed) color output image  Pixel values are triplets (RGB, CMY, HSI) or

quartets (CMYK)

docsity.com

9

Color transformations

ri: color component of f(x,y) at point (x,y) si: color component of g(x,y) at point (x,y) n: no. of color components (n=3 for RGB, HSI; n=4 for CMYK) T: {T1, T2,…,Tn} a set of n transformation (or color mapping) functions to perform risi

1 2( , , , ) for 1, 2,3, ,i i ns T r r r i n  

docsity.com

10

Color transformations: Example Task: To modify the intensity of the strawberry

image  Transformation in HSI color space

 Transformation in RGB color space

 Transformation in CMY(K) color space

1 1 1

2 2 2

3 3 3

: ( ) : ( ) : . ( ); 0 1

T s r Hue T s r Saturation T s k r Intensity for k

    

. ; 1, 2,3 ( , , )i i iT s k r i R G B  

. (1 ); 1,2,3 ( , , )i i iT s k r k i C M Y    

docsity.com

11

Color transformations: Example

docsity.com

12

Color transformations: Color complements

 Analogous to negative gray level transformation

docsity.com

13

Color complements: Example

docsity.com

14

Color transformations: Color slicing Highlighting a specific range of colors in an image  Basic idea is:

 Display the colors of interest so that they stand out from the background

 To use the region defined by the colors as mask for further processing

 If the color of interest are enclosed by a cube of width W and centered at (a1, a2, ….,an), the transformation is given by:

1

0.5 2

1, 2, ,

j j any j ni

i

Wif r a s

r otherwise for i n

 

           

docsity.com

15

Color transformations: Color slicing

 If the colors of interest are enclosed by a sphere of radius R0 and centered at (a1, a2, ….,an), the transformation is given by:

 2 20 1

0.5

1, 2, ,

n

j j ji

i

if r a R s

r otherwise for i n

   

  

Note: These transformation functions highlight the colors of interest only with gray background for all other colors

docsity.com

16

Color slicing: Example

docsity.com

17

Color transformations: Tone corrections  Tonal range or key type of an image

It is the general distribution of colors intensities in the image  Most of the information in high-key images is

concentrated at high intensities  The colors of low-key images are located at low

intensities  The color of middle-key images lie between high

and low intensities

docsity.com

18

Color transformations: Tonal transformations  Tonal transformations are selected interactively  The idea is to adjust experimentally the image’s

brightness and contrast to provide maximum detail over a suitable range of intensities

 During the transformation (or mapping), the colors themselves do not change  In RGB and CMYK spaces, this means mapping all three

(or four) color components with the same transformation function

 In HSI color space, only the intensity component is modified

docsity.com

19

Tonal correction for flat color image

Note: Adjusting the Red, Green and Blue components equally does not alter the image hue

docsity.com

20

Tonal correction for light (high- key) color image

Note: Adjusting the Red, Green and Blue components equally does not alter the image hue

docsity.com

21

Tonal correction for dark (low- key) color image

Note: Adjusting the Red, Green and Blue components equally does not alter the image hue

docsity.com

22

Color transformations: Color balancing

 Applied only after the tonal corrections

 Can be judged using visual assessment or with a color spectrometer

 Important: Every action affects the overall color balance of the image Use of color wheel (color complements) can be useful in the determination of overall balance

docsity.com

23

docsity.com

24

docsity.com

25

Color transformations: Histogram processing

 Histogram equalization on a gray level image results in an other gray level image with a uniform histogram of intensity values

 The same technique can be applied to color images but only on the intensity component e.g. Intensity component in HSI color space. Chromaticity components (Hue + Saturation) are left unchanged

docsity.com

26

a. A color image

b. Image’s intensity histogram

c. Result of histogram equalization on the intensity component (the overall color perception is also changed).

d. Result of increase in saturation (colors are now perfectly recovered)

Note: Changes in intensity usually affect the relative appearance of colors in an image

docsity.com

comments (0)
no comments were posted
be the one to write the first!
This is only a preview
3 shown on 26 pages
Download the document