EELE 5310: Digital Image Processing Lecture 1 Eng. Ruba A. Salamah Rsalamah @ iugaza.Edu

1. EELE 5310: Digital Image ProcessingLecture 1Eng. Ruba A. SalamahRsalamah @ iugaza.Edu

2. To Cover the basic theory and algorithms that are widely used in digital image processing. • To Expose students to current technologies and issues that are specific to image processing systems. • To Develop hands-on experience in using computers to process images. • Familiarize with MATLAB Image Processing Toolbox. Course Objectives:

3. • “Digital Image Processing” by R.C. Gonzalez and R.E. Woods, 3rd edition, Pearson Prentice Hall, 2008 • • Additional readings on the class website Recommended Textbook

4. Knowledge of the following three areas: -Linear Algebra. -Elementary Probability Theory. -Signals and Systems. Prerequisites:

5. Quizzes 15% • H.W 10% • Attendance 10% • Projects 20% • FinalExam45% Grading Policy

6. Introduction • Digital Image Fundamentals • Image Enhancement in the Spatial Domain • Image Enhancement in the Frequency Domain • Image Restoration • Image Compression • Image Segmentation • Representation and Description Course outline

7. Fundamentals of Digital Image Processing

8. A finite array of data values What is a Digital Image?

9. Processing digital images by means of a digital computer. • Image processing typically attempts to accomplish one of three things: • Restoring Images • Enhancing Images • Understanding Images • Restoration takes a corrupted image and attempts to recreate a clean original • Enhancement alters an image to makes its meaning clearer to human observers • Understanding usually attempts to mimic the human visual system in extracting meaning from an image What is Image Processing

10. Low-level Processes : • Involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. • A low-level process is characterized by the fact that both its inputs and outputs are images. • Mid-level Processes: • Involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for machine learning , and classification(recognition) of individual objects. • Its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Three Types of Processes

11. High-level Processes : • Processing involves "making sense“ of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision. Three Types of Processes

12. Processing of remote-sensed images via satellite. • Radar, MRI, Ultrasonic image processing. • Noise Reduction. • Character recognition. • Automatic inspection of industrial parts. • Content based image retrieval. • Biometrics. • Target tracking. Applications

13. The principle energy source for images is the EM spectrum Other sources include ultrasonic, electronic, and synthetic images. Sources of Energy for Image Formation

14. Some Applications -- Medical Diagnostics

15. Some Applications -- MRI Imaging in Radio Band

16. Some Applications -- Microscopy

17. Some Applications -- Industrial Inspection

18. Some Applications -- Remote Sensing

19. Some Applications -- Transmitting Images

20. Key Stages in Digital Image Processing

21. Image Acquisition

22. Image Enhancement

23. Image Restoration

24. Morphological Processing

25. Segmentation

26. Object Recognition

27. Image Representation & Description

28. Image Compression

29. Colour Image Processing

30. Image Acquisition

31. Digitalization of an analog signal involves two operations: • Sampling: Degitizing the x- and y-coordinates. • Quantization: Degitizing the amplitude values. Image Sampling and Quantization

32. Image Sampling and Quantization

33. A digital image is composed of M rows and N columns of pixels each storing a value. Representing Ddigital Images

34. A complete M × N digital image can be written in the following compact matrix form: • The right side of this equation is by definition a digital image. Each element of his matrix array is called an image element, picture element, pixel, or pel. Representing Ddigital Images

35. A digital image can be represented as a 2-D function whose coordinates and amplitude values are integers. • The digitization process requires decisions about values for M, N, and for the number, L, of discrete gray levels allowed for each pixel. • The discrete levels are equally spaced integers in the interval [0, L-1], this range is called the dynamic range of an image. • Images with high dynamic range will have high contrast and (vise versa). Storage Capacity

36. The number, b, of bits required to store a digitized image is: b = M x N x k Storage Capacity

37. The spatial resolution of an image is determined by how sampling was carried out Spatial resolution simply refers to the smallest discernable detail in an image Vision specialists well often talk about pixel size Graphic designers will talk about dots per inch (dpi) Spatial Resolution

38. Gray-level resolution is the smallest discernible change in gray level. • Due to hardware considerations, the number of gray levels is usually an integer power of 2. The most common number is 8 bits, i.e 256 levels. • It is common to refer to an L-level digital image of size MxN as having a spatial resolution of MxN pixels and a gray-level resolution of L levels. Gray-level Resolution

39. Effects of Varying Spatial Resolution

40. Effects of Varying Spatial Resolution

41. Effects of Varying Gray-Level Resolution Ridge like structure False contouring

42. Effects of Varying Gray-Level Resolution

43. As a very rough rule of thumb, and assuming powers of 2 for convenience, images of size 256*256 pixels and 64 gray levels are about the smallest images that can be expected to be reasonably free of objectionable sampling checkerboards and false contouring. Effects of Varying Gray-Level Resolution

44. Zooming (digital image) can be viewed by oversampling (continuous image). 1- Creation of new pixel locations 2- Assign a gray level value to this new location using: • Nearest neighbor interpolation (Pixel replication ) • Bilinear interpolation Zooming Digital Images

45. Applicable to increase the size of an image an integer number of times. • We can duplicate each column and each row. • New locations are duplicates of old locations. • Fast but produces checkerboard effect that is particularly objectionable at high factor of magnification. Pixel Replication x4

46. Using the four nearest neighbors of a point. Let (x’, y’) denote the coordinates of a point in the zoomed image, the gray value v(x’,y’) will be set to: V(x’,y’)=ax’ + by’ +cx’y’ + d Where the four coefficients are determined from the four equations in four unknowns using the four nearest neighbors of point (x’, y’). Bilinear Interpolation

47. Image Zooming

48. Shrinking (digital image) can be viewed by undersampling (continuous image). 1- Deletion of row column pixels. 2- Assign a gray level value using : • Nearest neighbor interpolation • Bilinear interpolation Shrinking Digital Image

49. 1- Neighbors of a Pixel: • The 4- neighbors of pixel p are: N4(p) • The 4- diagonal neighbors are: ND(p) • The 8-neighbors are : N8(p) Relationships Between Pixels

50. Connectivity between pixels is important Because it is used in establishing boundaries of objects and components of regions in an image • Two pixels are connected if: • They are neighbors (i.e. adjacent in some sense -- e.g. N4(p), N8(p), …) • Their gray levels satisfy a specified criterion of similarity (e.g. equality, …) Relationships Between Pixels