Fundamentals of Digital Image Processing

1. Fundamentals of Digital Image Processing

2. What is an Image An image is a 2D function, f(x,y) • Where x and y are spatial coordinates • Amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image. i.e. value of f(x,y) : proportional to the brightness of the image at (x,y) When spatial coordinates and amplitude values are all finite, discrete quantities, the image is called as digital image.

3. x Origin y f(x,y) Image “After snow storm” Fundamentals of Digital Images wAn image: a multidimensional function of spatial coordinates. wSpatial coordinate: (x,y) for 2D case such as photograph, (x,y,z) for 3D case such as CT scan images (x,y,t) for movies w The functionf may represent intensity (for monochrome images) or color (for color images) or other associated values.

4. Digital Images Digital image: an image that has been discretized both in Spatial coordinates and associated value. w Consist of 2 sets:(1) a point set and (2) a value set w Can be represented in the form I = {(x,a(x)): xÎX, a(x) ÎF} where X and F are a point set and value set, respectively. w An element of the image, (x,a(x)) is called a pixel where - x is called the pixel location and - a(x) is the pixel value at the location x

5. Digital Image Digital image = a multidimensional array of numbers (such as intensity image) or vectors (such as color image) Each component in the image called pixel associates with the pixel value (a single number in the case of intensity images or a vector in the case of color images).

6. An Digital Image

7. Types of Digital Image • Black-n-White image Binary image (two-tone) Gray level image (gray-tone) • Color image • Another classification Still image Movie image

8. Grayscale image Intensity image or monochrome image each pixel corresponds to light intensity normally represented in gray scale (gray level).

9. Binary image Binary image or black and white image Each pixel contains one bit : 1 represent white 0 represents black

10. Color image Color image or RGB image: each pixel contains a vector representing red, green and blue components.

11. Image Types : Index Image Index image Each pixel contains index number pointing to a color in a color table Color Table Index value

12. Dimensionality of Digital Images • Images and videos aremulti-dimensional(≥ 2 dimensions) signals. • In 3D case, each entry is called a volume element “voxel” Dimension 3 2D Image Dimension2 3-D Image Sequence or Video Dimension2 Dimension1 Dimension1

13. Three related sub-fields • Image processing • Computer vision • Computer graphics

14. Image Preprocessing Restoration Enhancement • Inverse filtering • Wiener filtering Spectral Domain Spatial Domain • Filtering • >>fft2/ifft2 • >>fftshift • Point Processing • >>imadjust • >>histeq • Spatial filtering • >>filter2

15. Image Processing

16. Imaging • The creation of visual representations of objects, such as a body parts or celestial bodies, for the purpose of medical diagnosis or data collection, using any of a variety of usually computerized techniques.

17. Computer Vision • A branch of artificial intelligence and image processing concerned with computer processing of images from the real world. • Computer vision typically requires a combination of low level image processing to enhance the image quality (e.g. remove noise, increase contrast) and higher level pattern recognition and image understanding to recognise features present in the image. The use of digital computer techniques to extract, characterize, and interpret information in visual images of a three-dimensional world.

18. Three types of computarized processes • 1. Low level: I/ Ps & O/ Ps are images • Primitive operations such as image preprocessing to reduce noise, contrast enhancement , smoothing & image sharpening • Mid level: I/Ps may be images, O/Ps are attributes extracted from those images Segmentation Description of objects Classification of individual objects 3. High level Image analysis

19. Image Processing • Mainly study these topics • Image acquisition – (low-level) digital representation of the world scenes • Image processing – noise removal, smoothing, sharpening, contrast enhancement, alter the appearance of an image • Image compression – efficiently represent image data for storage (save disk space) and communication (save network bandwidth) • Display – render the image data on reproduction media (monitors, printing papers)

20. Image Processing • Image acquisition – (low-level) digital representation of the world scenes 123 33 234 45 67 90 12 134 34 56 89 54 67 98 111 56 67 90 65 34 …. Numbers represent the brightness and colors of the world objects, but we have no knowledge what object, e.g., books, monitors, these numbers contain – hence low-level

21. Image Processing • Image acquisition – (low-level) digital representation of the world scenes 123 33 234 45 67 90 12 134 34 56 89 54 67 98 111 56 67 90 65 34 …. What numbers? How many numbers? How large/small should the numbers be?

22. Image Processing • Mainly study these topics 123 33 234 45 67 90 12 134 34 56 89 54 67 98 111 56 67 90 65 34 …. Numerical representation of the brightness and colors of the world scene The World

23. Image Processing • Image processing – noise removal, smoothing, sharpening, contrast enhancement, alter the appearance of an image Noise removal

24. Image Processing • Image processing – noise removal, smoothing, sharpening, contrast enhancement, alter the appearance of an image Blurring/smoothing

25. Image Processing • Image processing – noise removal, smoothing, sharpening, contrast enhancement, alter the appearance of an image Sharpening

26. Image Processing • Image processing – noise removal, smoothing, sharpening, contrast enhancement, alter the appearance of an image Contrast enhancement

27. Image Processing • Image processing – noise removal, smoothing, sharpening, contrast enhancement, alter the appearance of an image Alter appearance

28. Image Processing • Image compression – efficiently represent image data for storage (save disk space) and communication (save network bandwidth) 69,632 bytes 245,760 bytes 5,951 bytes

29. Image Processing • Display – render the image data on reproduction media (monitors, printing papers) 123 33 234 45 67 90 12 134 34 56 89 54 67 98 111 56 67 90 65 34 ….

30. Computer Vision • Mainly study these topics High level knowledge of the scene, e.g., Object ID, Scene structure, Indoor/outdoor scene Colors of the illumination etc Image representation The World

31. Computer Vision • Mainly study these topics High level knowledge Image Model

32. Computer Vision • Mainly study these topics • Building a mathematical model of the scene • Interpret the scene • Acquire high level knowledge of the scene, e.g., indoor/outdoor, man-made/nature • Detect the presence of certain objects, e.g., faces, cars • Recognize certain objects, e.g., person identification • And other related topics

33. Computer Graphics • Computer graphics are graphics created using computers and, more generally, the representation and manipulation of image data by a computer with help from specialized software and hardware. Model Image

34. Why do we Process Images • To facilitate their storage Efficient storage in digital cameras Video streaming on the internet • To prepare them for display or printing half-toning adjust Image size • To enhance or restore them Improve visibility of features Repair photographic errors • To extract information from them face recognition Aerial surveillance

35. 1-D signal Processing &multidimensional signal processing • Both involve such common operations as: • Filtering • Sampling • Transform computation and manipulation • Most of these operations generalize straightforwardly • The volume of data is larger

36. 1-D signal Processing & Image processing • Images are two dimensional Mathematics more limiting Mathematics more general • Images have finite extent • Notion of Causality goes away • Recursive systems are rarely used • Images are not zero mean • Nonlinear operations are more common • Fundamental theorem of algebra doesn’t hold in • MD SP

37. Image Processing Vs Computer Vision

38. Image processing tasks • Image Processing is concerned with lower level tasks Sampling and Quantization Noise removal Restoration Enhancement Geometric manipulation

39. Computer vision tasks • Computer vision is concerned with higher level tasks Morphological operations Edge Detection Feature extraction Shape analysis Image Detection and Registration

40. History of Digital Image Processing • Early 1920s: One of the first applications of digital • imaging was in the newspaper industry • The Bartlane cable picture transmission service • Images were transferred by submarine cable between London and New York • Pictures were coded for cable transfer and reconstructed at the receiving end on a telegraph printer

41. History of DIP (cont…) • Mid to late 1920s: Improvements to the Bartlane system resulted in higher quality images • New reproduction Processes based on photographic techniques • Increased number of tones in reproduced images

42. History of DIP (cont…) • 1980s - Today: The use of digital image processing techniques has exploded and they are now used for all kinds of tasks in all kinds of areas • Image enhancement/restoration • Artistic effects • Medical visualisation • Industrial inspection • Law enforcement • Human computer interfaces

43. IP Applications: Human Perception • Noise Filtering Transformations • Contrast Enhancement Astronomy • Image Deblurring Weather Forecasting • Image Correction Medical Imaging • Image Inpainting Artistic Effects • Image Fusion Document Image Analysis • Image Stitching Hyperspectral Imaging

44. Noise Filtering • The procedure of reducing the noise components of an image so as to enhance its information is known as Noise filtering.

45. Simultaneous Contrast • Which small square block is more darker? • All the small squares have exactly the same intensity, but they appear to the eye progressively darker as the background becomes brighter. • Region’s perceived brightness does not depend simply on its intensity

46. Examples: Image Enhancement • One of the most common uses of DIP techniques: improve quality, remove noise etc

47. Contrast Enhancement • Contrast enhancement increases the total contrast of an image by making light colors lighter and dark colors darker at the same time.

48. Examples: Artistic Effects

49. Examples: Medicine • Take slice from MRI scan of canine heart, and find boundaries between types of tissue • Image with gray levels representing tissue density • Use a suitable filter to highlight edges

50. Examples: Image Correction • Needed when image data is erroneous: Bad transmission Bits are missing Salt & Pepper Noise