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Image Restoration (Digital Image Processing)

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This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.

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Image Restoration (Digital Image Processing)

  1. 1. A Lecture onIntroduction toImage Restoration 10/22/2014 1 Presented By KalyanAcharjya Assistant Professor, Dept. of ECE Jaipur National University
  2. 2. Lecture on Image Restoration 2 By Kalyan Acharjya,JNUJaipur,India Contact :kalyan.acharjya@gmail.com 10/22/2014
  3. 3. 10/22/2014 3 Objective of Two Hour Presentation “TointroducethebasicconceptofImageRestorationinDigitalImageProcessing”
  4. 4. 10/22/2014 4 Sorry,shamelesslyIopenedthelockwithoutpriorpermissiontakenfromtheoriginalowner.SomeimagesusedinthispresentationcontentsarecopiedfromBook’swithoutpermission. OnlyOriginalOwnerhasfullrightsreservedforcopiedimages. ThisPPTisonlyforfairacademicuse. KalyanAcharjya
  5. 5. 10/22/2014 5 Acknowledgement •Gonzalez & Woods-DIP Books •Prof. P.K. Biswas, IIT Kharagpur •Dr. ir.AleksandraPizurika, UniversiteitHent. •GlebV. Teheslavski. •Yu Hen Yu. •Zhou Wang, University of Texas. The PPT was designed with the help of materials of following authors.
  6. 6. Outlines 10/22/2014 6 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  7. 7. Lets start 10/22/2014 7 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  8. 8. 10/22/2014 8 What is Image Restoration.
  9. 9. What is Image Restoration? 10/22/2014 9 Image restoration attempts to restore images that have been degraded Identify the degradation process and attempt to reverse it. Almost Similar to image enhancement, but more objective. Fig: Degraded image Fig: Restored image
  10. 10. Where we reached? 10/22/2014 10 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  11. 11. Image enhancement vs. Image Restoration 10/22/2014 11 •Imagerestorationassumesadegradationmodelthatisknownorcanbeestimated. •OriginalcontentandqualitydoesnotmeanGoodlookingorappearance. •ImageEnhancementissubjective,whereasimagerestorationisobjectiveprocess. •Imagerestorationtrytorecoveroriginalimagefromdegradedwithpriorknowledgeofdegradationprocess. •Restorationinvolvesmodelingofdegradationandapplyingtheinverseprocessinordertorecovertheoriginalimage. •Althoughtherestoreimageisnottheoriginalimage,itsapproximationofactualimage.
  12. 12. 10/22/201412 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications. Where we reached?
  13. 13. Degradation Model? 10/22/2014 13 Objective:Torestoreadegraded/distortedimagetoitsoriginalcontentandquality. Spatial Domain: g(x,y)=h(x,y)*f(x,y)+ŋ(x,y) Frequency Domain: G(u,v)=H(u,v)F(u,v)+ ŋ(u,v) Matrix: G=HF+ŋDegradation Function hRestoration Filters g(x,y) f(x,y) ŋ(x,y) f(x,y) ^ Degradation Restoration
  14. 14. Going On….! 10/22/2014 14 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  15. 15. Noise Models and Their PDF 10/22/2014 15 •Different models for the image noise term η(x, y) Gaussian Most common model Rayleigh Erlangor Gamma Exponential Uniform Impulse Salt and pepper noise GaussianRayleighErlang Exponential Uniform Impulse
  16. 16. Noise Models Effects 10/22/2014 16 Histogram to go here Fig: Original Image Fig: Original Image histogram
  17. 17. Noise Models Effects contd1… 10/22/2014 17
  18. 18. Noise Models Effects contd2… 10/22/2014 18
  19. 19. Going On….! 10/22/2014 19 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  20. 20. Estimation of Degradation Model. 10/22/2014 20 Weather the spatial or frequency domain or Matrix, in all cases knowledge of degradation function is important. Estimation of H is important in image restoration. There are mainly three ways to estimate the H as follows- By Observation By Experimentation. Mathematical Modeling •Afterapproximationthedegradationfunction,weapplytheBLINDCONVOLUTIONtorestoretheoriginalimage.
  21. 21. Observation 10/22/201421 Noknowledgeofdegradedfunctionisgiven. Observingong(x,y),trytoestimatethedegradedfunctionintheregionwhichhavesimplerstructure. gs(x,y)Gs(u,v) fs(x,y)Fs(u,v) Hs(u,v)=Gs(u,v)/Fs(u,v)
  22. 22. Observation contd… 10/22/2014 22gs(x,y) fs(x,y) Gs(u,v) Fs(u,v) Hs(u,v)= Gs(u,v)/ Fs(u,v)
  23. 23. Experimentation 10/22/201423 •Try to imaging set-up similar to original. Impulse response and impulse simulation. Objective to find H which have similar result of degradation as original one. Fig: Impulse Simulation Impulse Impulse Response
  24. 24. Experimentation contd… 10/22/201424 Here f(x,y) is impulse. F(u,v)=>A (a constant). G(u,v)=H(u,v)F(u,v). H(u,v)=G(u,v)/A. Objective is training and testing. Never testing on training data. Note:The intensity of impulse is very high, otherwise noise can dominate to impulse.
  25. 25. Mathematical Modeling 10/22/2014 25 If you have the mathematical model, you have inside the degradation process. Atmospheric turbulence can be possible to mapping in mathematical model. One e.g. of mathematical model k gives the nature of turbulence.
  26. 26. Mathematical Modeling contd.. Atmospheric Turbulence blur examples 10/22/2014 26 Fig: Negligible Turbulence Fig: Severe Turbulence, k=0.0025 Fig: Mid Turbulence, k=0.001 Fig: Low Turbulence, k=0.00025
  27. 27. Present Position 10/22/2014 27 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  28. 28. Restoration Techniques. 28 Inverse Filtering. Minimum Mean Squares Errors. Weiner Filtering. Constrained Least Square Filter. Non linear filtering Advanced Restoration Technique. 10/22/2014
  29. 29. Filter used for Restoration Process Mean filters Arithmetic mean filter Geometric mean filter Harmonic mean filter Contra-harmonic mean filter Order statistics filters Median filter Max and min filters Mid-point filter alpha-trimmed filters Adaptive filters Adaptive local noise reduction filter. Adaptive median filter 29 10/22/2014
  30. 30. Filtering to Remove Noise-AMF  Use spatial filters of different kinds to remove different kinds of noise  Arithmetic Mean :  This is implemented as the simple smoothing filter Blurs the image to remove noise.   s t Sxy g s t mn f x y ( , ) ( , ) 1 ˆ ( , ) 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 30 10/22/2014
  31. 31. Filtering to Remove Noise-GMF  Geometric Mean:  Achieves similar smoothing to the arithmetic mean, but tends to lose less image detail. mn s t Sxy f x y g s t 1 ( , ) ˆ ( , ) ( , )           31 10/22/2014
  32. 32. Filtering to Remove Noise-HMF  Harmonic Mean:  Works well for salt noise, but fails for pepper noise  Satisfactory result in other kinds of noise such as Gaussian noise   s t Sxy g s t mn f x y ( , ) ( , ) 1 ˆ ( , ) 32 10/22/2014
  33. 33. Filtering to Remove Noise-CHMF  Contra-harmonic Mean:  Q is the order of the filter and adjusting its value changes the filter’s behaviour.  Positive values of Q eliminate pepper noise.  Negative values of Q eliminate salt noise.       xy xy s t S Q s t S Q g s t g s t f x y ( , ) ( , ) 1 ( , ) ( , ) ˆ ( , ) 33 10/22/2014
  34. 34. Result of AMF and GMF 34 Fig: Original Image Fig: Gaussian Noise Fig: Result of 3*3 AM Fig: Result of 3*3 GM 10/22/2014
  35. 35. Result of Contra-harmonic Mean Filter 35 Fig: Original Image with Pepper noise Fig: Original Image with Salt noise Fig: After filter by 3*3 CHF, Q=1.5 Fig: After filter by 3*3 CHF, Q=-1.5 10/22/2014
  36. 36. Beware: Q value in Contra-harmonic Filter Choosing the wrong value for Q when using the contra-harmonic filter can have drastic results. 36 10/22/2014
  37. 37. Order Statistics Filters Spatial filters that are based on ordering the pixel values that make up the neighbourhood operated on by the filter Useful spatial filters include Median filter. Maximum and Minimum filter. Midpoint filter. Alpha trimmed mean filter. 37 10/22/2014
  38. 38. Median Filter  Median Filter:  Excellent at noise removal, without the smoothing effects that can occur with other smoothing filters  Best result for removing salt and pepper noise. ˆ ( , ) { ( , )} ( , ) f x y median g s t s t Sxy  38 10/22/2014
  39. 39. Maximum and Minimum Filter  Max Filter:  Min Filter:  Max filter is good for pepper noise and min is good for salt noise ˆ ( , ) max { ( , )} ( , ) f x y g s t s t Sxy  ˆ ( , ) min { ( , )} ( , ) f x y g s t s t Sxy  39 10/22/2014
  40. 40. Midpoint Filter  Midpoint Filter:  Good for random Gaussian and uniform noise         max { ( , )} min { ( , )} 2 1 ˆ ( , ) ( , ) ( , ) f x y g s t g s t xy xy s t S s t S 40 10/22/2014
  41. 41. Alpha-Trimmed Mean Filter  Alpha-Trimmed Mean Filter:  Here deleted the d/2 lowest and d/2 highest grey levels, so gr(s, t) represents the remaining mn – d pixels    s t Sxy r g s t mn d f x y ( , ) ( , ) 1 ˆ ( , ) 41 10/22/2014
  42. 42. Result of Median Filter 42 Fig 1: Salt & Pepper noise Fig2: Result of 1 pass Med 3*3 Fig3: Result of 2 pass Med 3*3 Fig4: Result of 3 pass Med 3*3 10/22/2014
  43. 43. Result of Max and Min Filter Fig: Corrupted by Pepper Noise Fig: Filtering Above,3*3 Max Filter 43Fig: Corrupted by Salt Noise Fig: Filtering Above,3*3 Min Filter 10/22/2014
  44. 44. Periodic Noise Typicallyarisesduetoelectricalorelectromagneticinterference. Givesrisetoregularnoisepatternsinanimage FrequencydomaintechniquesintheFourierdomainaremosteffectiveatremovingperiodicnoise 44 Fig: periodic Noise 10/22/2014
  45. 45. Band Reject Filters  Removing periodic noise form an image involves removing a particular range of frequencies from that image.  Band reject filters can be used for this purpose.  An ideal band reject filter is given as follows:                   2 1 ( , ) 2 ( , ) 2 0 2 1 ( , ) ( , ) 0 0 0 0 W if D u v D W D u v D W if D W if D u v D H u v 45 10/22/2014
  46. 46. Band Reject Filters contd.. The ideal band reject filter is shown below, along with Butterworth and Gaussian versions of the filter. Ideal BandReject Filter ButterworthBand RejectFilter (of order 1) GaussianBand RejectFilter 46 10/22/2014
  47. 47. Result of Band Reject Filter Fig: Corrupted by Sinusoidal NoiseFig: Fourier spectrum of Corrupted Image Fig: Butterworth Band Reject Filter Fig :Filtered image 4710/22/2014
  48. 48. Conclusions-What we learnt… Restore the original image from degraded image, if u have clue about degradation function, is called image restoration. The main objective should be estimate the degradation function. If you are able to estimate the H, then follow the inverse of degradation process of an image. Weather spatial or frequency domain. Spatial domain techniques are particularly useful for removing random noise. Frequency domain techniques are particularly useful for removing periodic noise. 48 10/22/2014
  49. 49. •AdaptiveProcessing Spatialadaptive Frequencyadaptive •NonlinearProcessing Thresholding,coring… Iterativerestoration •AdvancedTransformation/Modeling Advancedimagetransforms,e.g.,wavelet… Statisticalimagemodeling •BlindDeblurringorDeconvolution Advanced Image Restoration 10/22/2014
  50. 50. ForadvancedImageRestoration(AdaptiveFilteringorNonlinearFilteringetc.),PleasereferredthebookofGonzalezandWoods,“DigitalImageProcessing”,PearsonEducationoranyotherstandardDigitalImageProcessingBooks. OR Writemeanemail:kalyan.acharjya@gmail.com OR 10/22/2014
  51. 51. Lets conclude..! 10/22/2014 51 What is Image Restoration. Image Enhancement vs. Image Restoration. Image Degradation Model. Noise Models. Estimation of Degradation Model. Restoration Techniques. Some Basics Filter Advanced Image Restoration. Conclusions. Tools for DIP. Applications.
  52. 52. Conclusions-What we learnt… 10/22/2014 52 Restore the original image from degraded image, if u have clue about degradation function is called image restoration. The main objective should be estimate the degradation function. If you are able to estimate the H, then follow the inverse of degradation process of an image. Weather spatial or frequency domain. Spatial domain techniques are particularly useful for removing random noise. Frequency domain techniques are particularly useful for removing periodic noise.
  53. 53. 10/22/2014 53 Where you start ? Digital Image Processing !
  54. 54. Popular Image Processing Software Tools 10/22/2014 54 CVIP tools (Computer Vision and Image Processing tools) Intel Open Computer Vision Library Microsoft Vision SDL Library MATLAB KHOROS
  55. 55. 10/22/2014 55 Applications of Digital Image Processing?
  56. 56. Applications of Digital Image Processing 10/22/2014 56 Identification. Computer Vision or Robot vision. Steganography. Image Enhancement. Image Analysis in Medical. Morphological Image Analysis. Space Image Analysis. Bottling and IC Industry……….etc.
  57. 57. 10/22/2014 57 Email-kalyan.acharjya@gmail.com
  58. 58. 10/22/2014 58 https://twitter.com/Kalyan_online Email-kalyan.acharjya@gmail.com
  59. 59. 10/22/2014 59

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