1. Image Enhancement ارتقاء تصویر Enhancement Spatial Domain Frequency Domain

2. g( x, y) =T[f( x, y)]

3. Single pixel methods - Gray level transformations Example - Historgram equalization - Contrast stretching - Arithmetic/logic operations Examples - Image subtraction - Image averaging - Multiple pixel methods Examples Spatial filtering - Smoothing filters - Sharpening filters Types of Image Enhancement in the Spatial Domain

4. Gray Levels Transformations تبدیلات سطوح خاکستری where r = input intensity and s = output intensity

5. مکمل کردن تصویر ( تصاویر منفی ) Image Negative L = the number of gray levels Original digital mammogram Negative digital mammogram

6. کشش تمایز Contrast Stretching

7. Notice the slope of T(r) - if Slope > 1  Contrast increases - if Slope < 1  Contrast decrease - if Slope = 1  no change

9. Gray level slicing بخش بندی سطح خاکستری

10. پردازش بافت نگار Histogram Processing Histogram = Graph of population frequencies

11. حالات مختلف در هیستوگرام

12. بهینه سازی ( تعدیل ) هیستوگرام Histogram Equalization

14. Logic Operations عملیات منطقی Original image Image mask Result Region of Interest

15. Image Subtraction تفریق تصویر

17. متوسط گیری تصویر Image Averaging Application : Noise reduction (noise) Image averaging

18. Basics of Spatial Filtering Sometime we need to manipulate values obtained from neighboring pixels Example: How can we compute an average value of pixels in a 3x3 region center at a pixel z?

19. Step 1. Selected only needed pixels Basics of Spatial Filtering

20. Step 2. Multiply every pixel by 1/9 and then sum up the values 4 67 6 9 1 3 3 4 …… … … Mask or Window or Template

21. Examples of Spatial Filtering Masks Sobel operators 01 1 0 0 2 -2 -2 1 0 2 0 0 1 3x3 moving average filter 11 1 1 1 1 1 1 1 3x3 sharpening filter 8

22. Smoothing Linear Filter : Moving Average Application : noise reduction and image smoothing Disadvantage: lose sharp details

23. Laplacian Sharpening : How it works Intensity profile p(x)p(x) 1 st derivative 2 nd derivative Edge

24. Laplacian Sharpening : How it works Before sharpening p(x)p(x) After sharpening

25. First Order Partial Derivative: Sobel operators 01 1 0 0 2 -2 -2 1 0 2 0 0 1 P

26. First Order Partial Derivative: Image Gradient

27. P

28. Laplacian Operator 8 0 0 4 0 0 The center of the mask is positive 11 1 -8 1 1 1 1 1 10 0 -4 1 1 0 1 0 The center of the mask is negative or Application: Enhance edge, line, point Disadvantage: Enhance noise

29. Laplacian Operator