1. Image Enhancement in Spatial Domain Presented by : - Mr. Trushar Shah. ME/MC Department, U.V.Patel College of Engineering, Kherva

2. Today’s topics What is image enhancement? Approaches. Image processing in spatial domain. Implementation -Image negative -Contrast Stretching - Power law transformation -Dynamic range compression -Bit plane Slicing. -Gray level Slicing.

3. What is Image Enhancement? To process an image so that the result is more suitable than the original image for a specific application. Enhancement is the subjective process.

4. Approaches Image Enhancement Spatial Domain Frequency Domain Point Processing Filtering OR Masking

5. Approaches Spatial domain – direct manipulation of pixel. Frequency domain – Manipulation in frequency plane

6. Spatial domain  Image can be modeled by a continuous function of two variables : (x, y) co- ordinates of point/pixel.  The image function values correspond to the brightness/intensity at image point and generally denoted by f (x, y). f (x, y). x y

7. Spatial domain(cont.) Point processing : - -Independent of neighbors Masking : - -based on small sub image.

8. Image negative

9. N = Gmax - O

10. Contrast Stretching

11. Factor that causes low contrast images  Lack of dynamic range.  Poor illumination Algorithm Implementation

12. Power law Transformations 

13. Power law Transformations 

14. Compression of dynamic range

15. Compression of dynamic Range s = c. log(1+|r|) Log function scales [0,10^6] to [0,6]. c=255/6.

16. Bit plane slicing Separating each bit from pixel gray level, and gathering same for all pixel will generate bit plane. Monochrome images are made of the 8-bit planes.

18. Gray level Slicing Separating gray level range of interest to different level so that the region is highlighted.

20. Histogram The histogram of a digital image with intensity levels in the range [0,L-1] is a discrete function h(r k )=n k where, -r k is the kth intensity value. - n k is the number of pixel with intensity r k. Normalized Histogram:- -A normalized histogram is given by p(r k ) = n k /MN. -The sum of all components of normalized histogram is 1.

21. Histogram

23. Conclusion for Histogram Processing The whole span of gray levels should be used. Number of pixels for all the gray levels, should be equal. OR The probability of occurrence of all gray level should be uniform.