1. Pseudo / Color Image Processing Fasih ur Rehman

2. Color Image Processing Two major areas of Color Image Processing –Pseudo Color Image Processing Assigning colors to gray values based on a specified criterion –Full Color Image Processing Processing Image in color Further two categories –Process each component individually and then form a composite processed color image from the components. –Work with color pixels directly. In RGB system, each color point can be interpreted as a vector. c(x, y)=[c R (x, y), c G (x, y), c B (x, y)]

3. Intensity Slicing Using a plane at f(x, y)=l i to slice the image function into two levels. If an image is interpreted as a 3-D function, the method can be viewed as one of placing planes parallel to the coordinate plane of the image Each plane “slices “ the function in the area of intersection Two color images whose relative appearance can be controlled by moving the slicing plane up and down the gray-level axis Gray-level to color assignments are made according to the relation : f(x,y)= c k if f(x,y)  V k (P planes, P+1 intervals) Plane is useful for geometric interpretation of the intensity slicing technique

4. Intensity Slicing (Cont.)

5. An Alternate

6. Example The gray scale was divided into intervals and a different color was assigned to each region (Fig. 6.20) Is simple but powerful aid in visualization, especially if numerous images are involved

7. Example (Another)

8. Another Example

9. Gray Level to Color Transformation Achieve a wide range of pseudo color enhancement Perform three independent transformations on the gray levels of any input pixels; these results are fed separately into the red, green, and blue channels of a color television monitor. (Fig. 6.23) Can be based on smooth, nonlinear functions, which, as might be expected (based on a single monochrome image) Piecewise linear function (Fig. 6.19) Obtain various degrees of enhancement (Fig.6.24) –Changing the phase and frequency of each sinusoid can emphasize ranges in the gray scale

10. Pseudo Image Processing

12. Example

13. One way to combine the sensed image data is by how they show either differences in surface chemical composition or changes in thee way the surface reflects sunlight.

14. Basics of Full Color Image Processing Two categories:  Process each component individually and then form a composite processed image from the individually processed components  Work with color pixels directly Color pixel are vectors –Let C be an arbitrary vector Two conditions must be satisfied for pre- component and vector-based 1.Has to be applicable to both vectors and scalars 2.The operator must be independent of the other components