Basics of digital image processing

1. Basics of digital image processing Erkki Rämö

2. Digital image processing • Editing and interpreting of picture information • Examples: • Improving the visual quality of the image • Removing an error from the image • Automated interpretation of the image

3. Related disciplines

4. Group discussion 1 • Discuss application areas of digital image processing.

5. Where is image processing applied? • Biological research – cell studies

6. Lauri Toivio • Military research – interpretation of reconnaissance photos

7. Lauri Toivio • Document control – scanning, interpretation, archiving

8. Lauri Toivio • Industry automation – machine vision

9. Lauri Toivio • Forensics – Fingerprint analysis

10. Lauri Toivio • Medicine – x-ray image analysis

11. Lauri Toivio • Photography – Digital photography

12. Publishing

13. Erkki Rämö • Space investigation

14. Erkki Rämö • Remote Sensing

15. Erkki Rämö • Mapping (eg. Google street view)

16. Erkki Rämö • Film industry

17. Visual image • Light = electromagnetic radiation • Different wavelengths of light reflect from the object and absorb to the object in different ways, depending on objects surfaces construction and material • Reflecting light is perceived with the eye-brain visual system as an image • Wavelength of visual light is 400 - 700 nm

18. Perceiving of the visual image What is needed: • Light source • Light bulb radiates light of some color • Target • which reflects part of the light and absorbs the rest • Eye • receives the signal • signal is interpreted by brain

19. Spectrum of light nm 10-6 10 103 109 Cosmic rays Gamma rays X- rays Infra- red Micro- wave UV Radio Visible light 400 nm 700 nm

20. Group discussion2 • List imaging applications working in different wavelengths. • Can you find imaging using else than electromagnetic radiation

21. Eyesight • Eye, visual nervetrack and brains visual centre form the human visual system • There’s no visual system better than the eye • Some animal eyes are better than human eye • Examples of ‘analog’ image processing • A paddle in the water, refraction of light in the interface of two substances • Image restoration by eyeglasses

22. From optical image to a digital image

23. The construction of the eye Cross-section of the human eye

24. Comparison between an eye and a camera • Similarities: • In the eye image is drawn upside down to the retina • Pupil works like the iris of the camera • Retina, with two types of visual cells, rods (about 120 million) and cones(about 7 million) • Differencies: • Focus by changing the refraction of the lense by means of the radial deformation

25. Visual cells of the eye • Rods • thousands of times more sensitive than cones. • responsible of dark vision • Cones • Responsible of seeing the colors • Three kinds: sensitive for blue-purple, green and red-yellow. • Peaks of sensitivity are in the wavelengths of 447 nm, 540 nm ja 577 nm

26. Anatomy of the eye • In the area of accurate sight, in the middle of the yellow-spots fovea there are no rods but plenty of cones. • Outside the fovea, accuracy of vision is poor • 5° from the fovea – only half of the accuracy • Only a small area of field of vision is seen accurate • Moving the eyeball we can focus on different details

27. Anatomy of the eye 2 • Sensitivity of visualcells to alteration of lighting is logarithmic • Webers law JND=K*I Where K is constant and JND (Just Noticable Difference) Example: 100 W lighting 10 W power increment. In 1000W lighting we need 100W increment for same result • Image: Intensitymustbedoubled to notice the samevisualdifference

28. Visual cells react with one another • Mach Band Effect • Eye works like a high pass filter sharpening the details • On the edge of the tone slope, dark color seems lighter and light color seems darker

29. Influence of the background Simultaneous contrast

30. Influence of the background Simultaneous contrast

31. Frequency response • How small details are still visible? • Influences: • Number and positioning of cells, elasticity of the eye, brain response, intensity of light

32. Procedure classes of image processing • Procedures have been developed already in1960’s, though due to lack of computing power they were hard to implement • Some procedures enhance the quality of the image • Others pick and analyze information from the image

33. 5 Procedure classes 1. Image Enhancement 2. Image Restoration 3. Image Analysis 4. Image Compression 5. Image Synthesis

34. Image Enhancement • Most common procedure class • Can be used as independent enhancement method or as pre-operation for other methods, for example reducing the image before analysis

35. Erkki Rämö Image enhancement 2 • Goal is to enhance the visual quality of the image • contrast and brightness • noise reduction • sharpening

36. Lauri Toivio Example: adjusting contrast • Photoshop ”autolevels”, which implements the whole tone scale for the image

37. Image Restoration • Goal is to restore an image as original or • removal of known photographic error • Corrections: • Removal of geometric distortion • Removal of blur • Noise removal • Motion-blur removal

38. Example: enhancing sharpness • Photoshop ”Unsharp mask”

39. Image Analysis • As result there rarely is an image, but information about what’s in the image • Implemented in various tasks involving artificial vision

40. Example: Measuring of an object

41. Image compression • Goal is to compress image-information so that it would consume less space • Pros • needs less space • faster transfer • Methods: • lossless compression(max 2:1) • lossy compression(max 100:1) • Based on redundant information in the image

42. Lauri Toivio Example: JPEG-compression 183 KB 17 KB

43. Image Synthesis • Image is built out of other images or • Visualization of non-image information • Used when: • taking a picture is not physically possible • fast and/or slow events • modelling an object which does not exist • Examples: • 2D images of projection images mathematically • visualization of chart information as an image

44. Construction of image processing application • Application can be divided into unit tasks • Application construction: • Applications • Fundamental Classes • Operations • Process

45. Lauri Toivio Application level • Basic description of application • Example application: • Capture video image of cars licence plate • Process and interpret the signs on the plate • Check register if the vehicle has any offense

46. Image processing part • Process image and identify letters and numbers as an array • In short: Read the signs of the licence plate

47. Process classes • Divide application into unit tasks • Image enhancing: Improve the image quality • Image analysis: Interpret the letters and numbers of the plate ZHO-408

48. Operations • Image enhancement: Improve the image quality • Contrast alteration: steepen the contrast • Edge highlighting: Outlines of signs • Image analysis: Interpretation of the letters and numbers of the plate • Detaching edges: Follow the outlines • Classification of objects: Fit vectors into images in model library

49. Methods • Contrast alteration: steepening of contrast • Contrast stretching as pixel operation • Edge highlighting: Outlines of symbols • Sobels edge highlighting algorithm • Finding edges: Follow the outlines • Edge finding algorithm • Classification of vectors: Fit vectors into images in model library