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ECES 682 Digital Image Processing

ECES 682 Digital Image Processing. Oleh Tretiak ECE Department Drexel University. 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

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ECES 682 Digital Image Processing

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

  2. 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 • 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 • Also see textbook website, imageprocessingplace.com Digtial Image Processing, Spring 2006

  3. 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 Digtial Image Processing, Spring 2006

  4. 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 Digtial Image Processing, Spring 2006

  5. 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. Digtial Image Processing, Spring 2006

  6. Conventions: Digital Images Left: Digital image. Note unusual (x, y) convention. Below: Examples of gray-value transformations. Digtial Image Processing, Spring 2006

  7. Basic Gray Level Transformations • Negative • Log • Power law • Piecewise linear • Bit slicing Digtial Image Processing, Spring 2006

  8. Histogram Processing • The histogram • Histogram processing • Histogram equalization • Global • Local • Histogram matching • Local means, variances Digtial Image Processing, Spring 2006

  9. 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 Digtial Image Processing, Spring 2006

  10. 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: Digtial Image Processing, Spring 2006

  11. Smoothing Masks • Smoothing masks are normally adjusted to preserve average value (∑wi = 1) Digtial Image Processing, Spring 2006

  12. Order Statistics Filters • R = median(z1, … zn) • R = max (z1, … zn) • R = min (z1, … zn) Digtial Image Processing, Spring 2006

  13. Sharpening Filters • One-dimensional • Two-dimensional (Laplacian) Digtial Image Processing, Spring 2006

  14. Laplacian Masks Digtial Image Processing, Spring 2006

  15. Image Sharpening (a) (b) Orignal Image, Laplacian, (c) Laplacian – scaled, (d) Original plus Laplacian (c) (d) Digtial Image Processing, Spring 2006

  16. Unsharp Masking/High Boosting • Unsharp masking is a technique developed in film chemical processing. An out-of-focus image was subtracted from the original. Digtial Image Processing, Spring 2006

  17. First Derivative Enhancement • There is no first derivative linear filter that is not direction-dependent • Magnitude of the gradient is independent of direction Digtial Image Processing, Spring 2006

  18. Some Implementations Upper masks: Roberts filter. Lower masks: Sobel Filter Digtial Image Processing, Spring 2006

  19. Other Examples • Unsharp masking with rank filters • Product masks (image times Sobel) • Combine with power-law transformation • ... Digtial Image Processing, Spring 2006

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

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

  22. Digtial Image Processing, Spring 2006

  23. Digtial Image Processing, Spring 2006

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