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Image processing3 imageenhancement(histogramprocessing)


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Image processing3 imageenhancement(histogramprocessing)

  1. 1. Course Website: Image ProcessingImage Enhancement(Histogram Processing)
  2. 2. 2of32Come To The LABS!Day: WednesdayTime: 9:00 – 11:00Room: Aungier St. 1-005We will start by getting to grips with thebasics of Scilab– Lab details available at WebCTShortly, there will be a Scilab assignmentwhich will count towards your final mark
  3. 3. 3of32ContentsOver the next few lectures we will look atimage enhancement techniques working inthe spatial domain:– What is image enhancement?– Different kinds of image enhancement– Histogram processing– Point processing– Neighbourhood operations
  4. 4. 4of32A Note About Grey LevelsSo far when we have spoken about imagegrey level values we have said they are inthe range [0, 255]– Where 0 is black and 255 is whiteThere is no reason why we have to use thisrange– The range [0,255] stems from display technologesFor many of the image processingoperations in this lecture grey levels areassumed to be given in the range [0.0, 1.0]
  5. 5. 5of32What Is Image Enhancement?Image enhancement is the process ofmaking images more usefulThe reasons for doing this include:– Highlighting interesting detail in images– Removing noise from images– Making images more visually appealing
  6. 6. 6of32Image Enhancement ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  7. 7. 7of32Image Enhancement Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  8. 8. 8of32Image Enhancement Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  9. 9. 9of32Image Enhancement Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  10. 10. 10of32Spatial & Frequency DomainsThere are two broad categories of imageenhancement techniques– Spatial domain techniques• Direct manipulation of image pixels– Frequency domain techniques• Manipulation of Fourier transform or wavelettransform of an imageFor the moment we will concentrate ontechniques that operate in the spatialdomain
  11. 11. 11of32Image HistogramsThe histogram of an image shows us thedistribution of grey levels in the imageMassively useful in image processing,especially in segmentationGrey LevelsFrequencies
  12. 12. 12of32Histogram ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  13. 13. 13of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  14. 14. 14of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  15. 15. 15of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  16. 16. 16of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  17. 17. 17of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  18. 18. 18of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  19. 19. 19of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  20. 20. 20of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  21. 21. 21of32Histogram Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  22. 22. 22of32Histogram Examples (cont…)A selection of images andtheir histogramsNotice the relationshipsbetween the images andtheir histogramsNote that the high contrastimage has the mostevenly spaced histogramImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  23. 23. 23of32Contrast StretchingWe can fix images that have poor contrastby applying a pretty simple contrastspecificationThe interesting part is how do we decide onthis transformation function?
  24. 24. 24of32Histogram EqualisationSpreading out the frequencies in an image(or equalising the image) is a simple way toimprove dark or washed out imagesThe formula for histogramequalisation is given where– rk: input intensity– sk: processed intensity– k: the intensity range(e.g 0.0 – 1.0)– nj: the frequency of intensity j– n: the sum of all frequencies)( kk rTs =∑==kjjr rp1)(∑==kjjnn1
  25. 25. 25of32Equalisation Transformation FunctionImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  26. 26. 26of32Equalisation ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)1
  27. 27. 27of32Equalisation Transformation FunctionsThe functions used to equalise the imagesin the previous exampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  28. 28. 28of32Equalisation ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)2
  29. 29. 29of32Equalisation Transformation FunctionsThe functions used to equalise the imagesin the previous exampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  30. 30. 30of32Equalisation Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)34
  31. 31. 31of32Equalisation Examples (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)34
  32. 32. 32of32Equalisation Transformation FunctionsThe functions used to equalise the imagesin the previous examplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  33. 33. 33of32SummaryWe have looked at:– Different kinds of image enhancement– Histograms– Histogram equalisationNext time we will start to look at pointprocessing and some neighbourhoodoperations