1. Digtial Image Processing, Spring 2006 1 ECES 682 Digital Image Processing Oleh Tretiak ECE Department Drexel University

2. Digtial Image Processing, Spring 20062 About the Course Instructor: Oleh Tretiak, Bossone 607, 215 895 2214, tretiak@coe.drexel.edu Office hours: M 2-4, Tu 2-4, or by appointment tretiak@coe.drexel.edu Textbook: Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing (Second Edition), Prentice Hall, 2002 Web site: ece.drexel.edu/courses/ECE-S682  Site contains syllabus, assignments, solutions, exams, etc  We will also use webct (reachable through Drexel One or via http://vle.dcollege.net/) for grade distribution http://vle.dcollege.net/  Also see textbook website, imageprocessingplace.com

3. Digtial Image Processing, Spring 20063 This Weeks Lecture Image Enhancement in the Spatial Domain  Gray level transformations  Histogram processing  Arithmetic/Logic operations  Spatial filtering  Smoothing  Sharpening Matlab image processing  Image datatypes  Image display  Color maps

4. Digtial Image Processing, Spring 20064 Intensity Scale What does ‘image intensity’ mean? In technical images, image intensity is reflects an objective quantity  In astronomy, intensity reflects energy per sterradian  In transmission microscopy, intensity is a function of amount of absorbing material on a ray passing through an object In most images, image intensity is a feature that allows us to infer the presence of objects in a scene. For human vision, the image is reflected by ‘intensity’ and ‘color’. Most of the time, intensity is much more important than color

5. Digtial Image Processing, Spring 20065 General Framework We compute a new image from an original image The most basic transformation is g(x, y) = T(f(x, y)) where f(x, y) is the gray value of the input image pixel, g(x, y) is the gray value of the input image pixel at the same locations, and T() is a function of a single (real) variable.

6. Digtial Image Processing, Spring 20066 Conventions: Digital Images Left: Digital image. Note unusual (x, y) convention. Below: Examples of gray-value transformations.

7. Digtial Image Processing, Spring 20067 Basic Gray Level Transformations Negative Log Power law Piecewise linear Bit slicing

8. Digtial Image Processing, Spring 20068 Histogram Processing The histogram Histogram processing Histogram equalization  Global  Local Histogram matching Local means, variances

9. Digtial Image Processing, Spring 20069 Arithmetic/Logical Operations Logical operations: x is a 4 bit number)  AND(x, 1111) = x  AND(x, 0000) = 0  OR(x, 1111) = 1111  OR(x, 1111) = x Subtraction: change detection Addition: Image averaging

10. Digtial Image Processing, Spring 200610 Spatial Filtering How big should a, b be? What do we do at edges? What are we trying to accomplish?  Smoothing  Edge detection Alternate notation:

11. Digtial Image Processing, Spring 200611 Smoothing Masks Smoothing masks are normally adjusted to preserve average value (∑w i = 1)

12. Digtial Image Processing, Spring 200612 Order Statistics Filters R = median(z 1, … z n ) R = max (z 1, … z n ) R = min (z 1, … z n )

13. Digtial Image Processing, Spring 200613 Sharpening Filters One-dimensional Two-dimensional (Laplacian)

14. Digtial Image Processing, Spring 200614 Laplacian Masks

15. Digtial Image Processing, Spring 200615 Image Sharpening (a)Orignal Image, (b)Laplacian, (c) Laplacian – scaled, (d) Original plus Laplacian (a) (d)(c) (b)

16. Digtial Image Processing, Spring 200616 Unsharp Masking/High Boosting Unsharp masking is a technique developed in film chemical processing. An out-of-focus image was subtracted from the original.

17. Digtial Image Processing, Spring 200617 First Derivative Enhancement There is no first derivative linear filter that is not direction-dependent Magnitude of the gradient is independent of direction

18. Digtial Image Processing, Spring 200618 Some Implementations Upper masks: Roberts filter. Lower masks: Sobel Filter

19. Digtial Image Processing, Spring 200619 Other Examples Unsharp masking with rank filters Product masks (image times Sobel) Combine with power-law transformation...

20. Digtial Image Processing, Spring 2006 20 Objective value (intensity) Subjective (perceived) value Mach Bands

21. Digtial Image Processing, Spring 2006 21 The circles have the same objective intensity.

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