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Red Eye Removal
     y


                         Tony Meccio


       Red eye removal   17/04/2008
Overview                                                2/26



   • I t d ti
     Introduction t the Red Eye problem
                  to th R d E      bl
   • Red Eye prevention
   • Red Eye detection
      • Semi-automatic methods
      • Automatic methods
   •R dE
    Red Eye correction
                  ti
      • Desaturation
      • Inpainting techniques
   • False positives and unnatural corrections
      a    po        a du au a o           o
   • Red Eye removal examples


                                Red eye removal   17/04/2008
The Red Eye problem                                      3/26



 The Red E
 Th R d Eye phenomenon is a well-
               h            i      ll
 known problem which happens
 when taking flash-lighted p
            g        g     pictures
 of people.


 The pupils in the picture appear red
 instead of black.


 This h
 Thi happens more often when
                    ft    h
 using compact, consumer-oriented
 cameras.
  a     a




                                 Red eye removal   17/04/2008
Why the Red Eye?                                              4/26



                        Red Eye Cone
        Eye

                           α      β




   • The red eye cone shines from the flashed eye
   back at the flash with an angle α;
                               g    ;
   • Its red color is caused by the reflection of the
   flash off the blood vessels o the retina;
     a o         b ood         of           a;
   • The camera will record this red hue if the angle β
   be ee
   between the flash and the camera is not greater
             e as a d e ca e a s o g ea e
   than α.


                               Red eye removal          17/04/2008
Red Eye prevention                                           5/26


        Eye
                           Red Eye Cone

                          α
                                  β




    • If the equipment allows it, the flash can be spaced
    further away from the sensor in order to increase
                 y
    β (not possible on compact devices);
    • One o more additional flashes before picture
      O   or o add o a a              b o p u
    acquisition make the iris tighten and decrease α;
    • This methods reduce the probability of the Red
         s e ods educe e p obab y o            e ed
    Eye phenomenon but don’t remove it entirely.


                               Red eye removal         17/04/2008
Red Eye detection                                             6/26



    Most f th ti
    M t of the times, red eyes must be removed
                        d         tb         d
    during post-processing.
    For d
    F red eyes to be successfully removed, they must
                 t b          f ll      d th       t
    be first detected then corrected.


    Methods are classified according to the detection
    phase:
     h
       • Semi-automatic methods ask the user to
       manually l
             ll localize th red eyes;
                    li the d
       • Automatic methods detect the red eyes
       themselves.
       th    l



                              Red eye removal           17/04/2008
Semi-automatic methods                            7/26


             The eyes are manually selected
             using a visual interface (
                 g                    (Adobe
             Photoshop ®, Corel Paint Shop
             Pro ®, ACD See ®, etc.)
             Pros:
                • Eyes are easy to localize for
                   y          y
                men.
             Cons:
                • It may be difficult to have
                such an interface on a mobile
                device;
                • Automatic methods are easier
                   uo a         od a     a
                to use and more appealing.


                     Red eye removal     17/04/2008
Automatic methods                                          8/26



   Automatic methods attempt to find red eyes on
   A t     ti    th d tt      t t fi d d
   their own. The task is harder than it may seem:
   No “
   N “perfect” R d E
          f t” Red Eye d t ti
                       detection method has
                                   th d h
   been developed yet.
   Automatic methods extract features from images in
   A t     ti     th d    t   tf t       f    i      i
   order to identify red eyes. Different methods work
   on different features:


      • Face detection           • Skin detection
      • Eye detection            • Flash-noFlash
                                 comparison



                             Red eye removal         17/04/2008
Face-based methods                                               9/26



   •F
    Faces are looked for using a multiple feature
              l k df       i       lti l f t
   object based approach;
   •O
    Once th f
          the face features have been found then the
                   f t      h    b    f   d th   th
   research is restricted to red pixels.




       Face
     Extraction


                     Extracted           Multiple Masks
                       Area             Red-Eye
                                        Red Eye Research




                                 Red eye removal           17/04/2008
Eye-based methods                                             10/26



   Similar t face detection, but more complex
   Si il to f     d t ti     b t          l
   because the features are less evident:
   •EEyes are seeked, matching fi d t
                 k d      t hi   fixed templates at
                                             l t    t
   different resolutions with regular eyes present
   into the images, or looking for red pixels using
               g ,            g        p          g
   computed color LUTs.



      Initial           Single Eye
                            g    y          Pairingg
    Candidate
                        Verification      Verification
    Detection




                               Red eye removal           17/04/2008
Skin-based methods                                                11/26



   • Ski i d t t d first by pixel colors;
     Skin is detected fi t b i l l
   • Red circular patches near the skin are then
   looked for.
   l k df
   This approach is simpler and does not take into
   account th presence of more complex features.
          t the           f           l   f t


       Skin
     Detection         Red Circular           Pairingg
                         Patches            Verification

                        Research


                                                       Optional




                              Red eye removal              17/04/2008
Flash-noFlash methods                                       12/26


 • Two different pictures,
 one with flash and one
        ith fl h     d
 without, are acquired
 one after the other;;
 • Red eyes are detected
 as patches whose color is
    p
 red in the “flash”
 image and black in the
 “nonflash” i
 “    fl h” image.
      This approach has several drawbacks:
          • The dimension of the buffer must double;
          • The two images may be mis-aligned;
              e   o   ages ay       s a g ed;
          • The subject(s) may move between acquisitions.


                                Red eye removal        17/04/2008
An algorithm in detail                                                13/26


                                             Red Color
  Input        Skin        Morphological    Detection in
             Detection      Operators        Skin Area
  Image

                                                            Morphological
                                                             Operators


 Corrected     Red-Eye         Pairing       Single Eye
                             Verification    Verification
  Image
     g        Correction



 The Algorithm is Skin Feature Extraction based.
     • First the skin is extracted and morphologically
     modified to blob the enhanced areas;
     • Then a successive red color detection is performed to find
     red eyes in the skin areas;
     • The red regions are then dilated, eroded and analyzed
     to identify the Red-Eyes pairs.

                                      Red eye removal        17/04/2008
Example                                                                                  14/26




  Input Image            Skin Detection          Morphological       Red Color Detection
                                                  Operators
                                                                          in Skin Area




         Morphological                  Pairing                  Corrected
          Operators                   Verification
                                                                  Image




                                             Red eye removal                 17/04/2008
Input/Output comparison               15/26




               Red eye removal   17/04/2008
Algorithm drawbacks                                        16/26



   • U bl t perform single red eye detection;
     Unable to f     i l     d     d t ti


   • The skin detection is performed over the whole
   image (slow);


   • Big (
       g (slow) morphological operators p
              )    p     g      p       permit to g
                                                  get
   good results only on small images (less than 1
   Mpixels): it would require even bigger (and slower)
   operators t operate on larger images.
         t    to      t     l     i




                             Red eye removal          17/04/2008
Red eye correction                                        17/26



    Once red eyes have been detected, they must be
    O       d     h    b    d t t d th        tb
    corrected.


    Red eye correction is quite simpler than detection,
    but there are more diffi lt cases than others.
    b t th              difficult     th    th


    Correction may vary from simple desaturation to
    complete reconstruction of iris and pupil.




                              Red eye removal        17/04/2008
Desaturation                                             18/26




 Desaturation means lowering/zeroing the chrominance
 components while mantaining the luminance component
                                           component.
 It is the best way to correct “easy” red eyes.


                                  Red eye removal   17/04/2008
Washed-out irises                                             19/26




     Washed-out irises                Wrong correction


 Sometimes irises are totally washed out by reflected light.
 In these cases a simple desaturation or color correction is not
 enough.


 It is necessary to use a more complex method to reconstruct a
 realistic image of the eye.



                                 Red eye removal        17/04/2008
Inpainting techniques                                          20/26




 Some tools completely reconstruct the irises and the pupils
 to
 t replace the red eye (Jasc Paint Shop Pro ®).
      l    th    d     (J    P i t Sh   P ®)
 The results, however, are often unrealistic and look like glass
 eyes.



                                  Red eye removal        17/04/2008
False positives                                                21/26

  One of the biggest issue in red eye removal are false
  p
  positives in the detection phase.
                             p



 Unwanted
 corrections are
 much less
 desirable than
 missing ones
          ones.




                                 Red eye removal          17/04/2008
Unnatural corrections                                          22/26


 • Unnatural corrections are another
   important issue.
 • The most common ones are:
    • Partial correction: only a
                                          Partial correction
      portion of the red pupil has
                f
      been corrected;
    • Noisy correction: the presence
      of heavy noise or jpeg
      compression can introduce false
      red pixels around the pupil and     Noisy correction
      thus a strange correction is
      made over the iris;
    • Wrong luminance correction:
      in this case the disk has been
      correctly found but the
      correction is unnatural due to     Wrong luminance
      wrong luminance distribution.         correction


                               Red eye removal       17/04/2008
Red Eye removal examples                  23/26




             StopRedeyes®      AutoRemover®




                 Red eye removal    17/04/2008
Red Eye removal examples                  24/26




            StopRedeyes®        AutoRemover®




                  Red eye removal    17/04/2008
Red Eye removal examples                    25/26




     StopRedeyes®            AutoRemover®



                    Red eye removal   17/04/2008
References                                                                                     26/26

 • J.Y. Hardeberg, “Red Eye Removal using Digital Color Image Processing”, Conexant
 Systems, Inc. , Redmond, Washington, USA
 • M. Gaubatz, R. Ulichney, “Automatic Red-Eye Detection and Correction”, HP Lab, Cornell
 University,
 University ICIP 2002
 • S. Ioffe, “Red Eye Detection With Machine Learning”, Fujifilm Software, California, ICIP
 2003.
 • F Gasparini, R. Schettini “Automatic Redeye Removal for Smart Enhancement of Photos of
   F. Gasparini R Schettini, Automatic
 Unknown Origin”, DISCO University of Milano-Bicocca, VIS 2005.
 • H. Luo, J. Yen, D. Tretter, “An Efficient Redeye Detection and Correction Algorithm”, HP
 labs, Palo Alto, California, ICPR 2004.
 • A. Patti, K. Konstantinides, D. Tretter, Q. Lin, “Automatic Digital Redeye Reduction”, HP
 Labs, Palo Alto California, ICIP 1998.
 • L. Zhang, Y. Sun, M. Li, H. Zhang, “Automated Red-Eye Detection and Correction in Digital
 Photographs”,
 Photographs” Microsoft Research Asia China ICIP 2004
                                      Asia, China,
 • B. Smolka, K. Czubin, J.Y. Hardeberg, K.N. Palataniotis, M. Szczepanski, K. Wojciechowski,
 “Towars Automatic Redeye Effect Removal”, Pattern Recognition Letter 2003.
 • J S Schildkraut R T Gray “A Fully Automatic Redeye Detection and Correction Algorithm”,
   J.S. Schildkraut, R. T. Gray, A                                             Algorithm
 Kodak Company, NY, ICIP 2002.
 • R. Schettini, F. Gasparini, F. Chazli, ”A Modular Procedure for Automatic Redeye Correction
 in Digital Photos”, DISCO University of Milano-Bicocca, SPIE 2004
 • PETSCHNIGG, G., AGRAWALA, M., HOPPE, H., SZELISKI, R., COHEN, M., and TOYAMA, K.
 2004. Digital photography with flash and no-flash image pairs. ACM Transactions on Graphics
 23, 3 (Aug.), 664–672.



                                                  Red eye removal                   17/04/2008

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Red Eye Removal

  • 1. 1/26 Red Eye Removal y Tony Meccio Red eye removal 17/04/2008
  • 2. Overview 2/26 • I t d ti Introduction t the Red Eye problem to th R d E bl • Red Eye prevention • Red Eye detection • Semi-automatic methods • Automatic methods •R dE Red Eye correction ti • Desaturation • Inpainting techniques • False positives and unnatural corrections a po a du au a o o • Red Eye removal examples Red eye removal 17/04/2008
  • 3. The Red Eye problem 3/26 The Red E Th R d Eye phenomenon is a well- h i ll known problem which happens when taking flash-lighted p g g pictures of people. The pupils in the picture appear red instead of black. This h Thi happens more often when ft h using compact, consumer-oriented cameras. a a Red eye removal 17/04/2008
  • 4. Why the Red Eye? 4/26 Red Eye Cone Eye α β • The red eye cone shines from the flashed eye back at the flash with an angle α; g ; • Its red color is caused by the reflection of the flash off the blood vessels o the retina; a o b ood of a; • The camera will record this red hue if the angle β be ee between the flash and the camera is not greater e as a d e ca e a s o g ea e than α. Red eye removal 17/04/2008
  • 5. Red Eye prevention 5/26 Eye Red Eye Cone α β • If the equipment allows it, the flash can be spaced further away from the sensor in order to increase y β (not possible on compact devices); • One o more additional flashes before picture O or o add o a a b o p u acquisition make the iris tighten and decrease α; • This methods reduce the probability of the Red s e ods educe e p obab y o e ed Eye phenomenon but don’t remove it entirely. Red eye removal 17/04/2008
  • 6. Red Eye detection 6/26 Most f th ti M t of the times, red eyes must be removed d tb d during post-processing. For d F red eyes to be successfully removed, they must t b f ll d th t be first detected then corrected. Methods are classified according to the detection phase: h • Semi-automatic methods ask the user to manually l ll localize th red eyes; li the d • Automatic methods detect the red eyes themselves. th l Red eye removal 17/04/2008
  • 7. Semi-automatic methods 7/26 The eyes are manually selected using a visual interface ( g (Adobe Photoshop ®, Corel Paint Shop Pro ®, ACD See ®, etc.) Pros: • Eyes are easy to localize for y y men. Cons: • It may be difficult to have such an interface on a mobile device; • Automatic methods are easier uo a od a a to use and more appealing. Red eye removal 17/04/2008
  • 8. Automatic methods 8/26 Automatic methods attempt to find red eyes on A t ti th d tt t t fi d d their own. The task is harder than it may seem: No “ N “perfect” R d E f t” Red Eye d t ti detection method has th d h been developed yet. Automatic methods extract features from images in A t ti th d t tf t f i i order to identify red eyes. Different methods work on different features: • Face detection • Skin detection • Eye detection • Flash-noFlash comparison Red eye removal 17/04/2008
  • 9. Face-based methods 9/26 •F Faces are looked for using a multiple feature l k df i lti l f t object based approach; •O Once th f the face features have been found then the f t h b f d th th research is restricted to red pixels. Face Extraction Extracted Multiple Masks Area Red-Eye Red Eye Research Red eye removal 17/04/2008
  • 10. Eye-based methods 10/26 Similar t face detection, but more complex Si il to f d t ti b t l because the features are less evident: •EEyes are seeked, matching fi d t k d t hi fixed templates at l t t different resolutions with regular eyes present into the images, or looking for red pixels using g , g p g computed color LUTs. Initial Single Eye g y Pairingg Candidate Verification Verification Detection Red eye removal 17/04/2008
  • 11. Skin-based methods 11/26 • Ski i d t t d first by pixel colors; Skin is detected fi t b i l l • Red circular patches near the skin are then looked for. l k df This approach is simpler and does not take into account th presence of more complex features. t the f l f t Skin Detection Red Circular Pairingg Patches Verification Research Optional Red eye removal 17/04/2008
  • 12. Flash-noFlash methods 12/26 • Two different pictures, one with flash and one ith fl h d without, are acquired one after the other;; • Red eyes are detected as patches whose color is p red in the “flash” image and black in the “nonflash” i “ fl h” image. This approach has several drawbacks: • The dimension of the buffer must double; • The two images may be mis-aligned; e o ages ay s a g ed; • The subject(s) may move between acquisitions. Red eye removal 17/04/2008
  • 13. An algorithm in detail 13/26 Red Color Input Skin Morphological Detection in Detection Operators Skin Area Image Morphological Operators Corrected Red-Eye Pairing Single Eye Verification Verification Image g Correction The Algorithm is Skin Feature Extraction based. • First the skin is extracted and morphologically modified to blob the enhanced areas; • Then a successive red color detection is performed to find red eyes in the skin areas; • The red regions are then dilated, eroded and analyzed to identify the Red-Eyes pairs. Red eye removal 17/04/2008
  • 14. Example 14/26 Input Image Skin Detection Morphological Red Color Detection Operators in Skin Area Morphological Pairing Corrected Operators Verification Image Red eye removal 17/04/2008
  • 15. Input/Output comparison 15/26 Red eye removal 17/04/2008
  • 16. Algorithm drawbacks 16/26 • U bl t perform single red eye detection; Unable to f i l d d t ti • The skin detection is performed over the whole image (slow); • Big ( g (slow) morphological operators p ) p g p permit to g get good results only on small images (less than 1 Mpixels): it would require even bigger (and slower) operators t operate on larger images. t to t l i Red eye removal 17/04/2008
  • 17. Red eye correction 17/26 Once red eyes have been detected, they must be O d h b d t t d th tb corrected. Red eye correction is quite simpler than detection, but there are more diffi lt cases than others. b t th difficult th th Correction may vary from simple desaturation to complete reconstruction of iris and pupil. Red eye removal 17/04/2008
  • 18. Desaturation 18/26 Desaturation means lowering/zeroing the chrominance components while mantaining the luminance component component. It is the best way to correct “easy” red eyes. Red eye removal 17/04/2008
  • 19. Washed-out irises 19/26 Washed-out irises Wrong correction Sometimes irises are totally washed out by reflected light. In these cases a simple desaturation or color correction is not enough. It is necessary to use a more complex method to reconstruct a realistic image of the eye. Red eye removal 17/04/2008
  • 20. Inpainting techniques 20/26 Some tools completely reconstruct the irises and the pupils to t replace the red eye (Jasc Paint Shop Pro ®). l th d (J P i t Sh P ®) The results, however, are often unrealistic and look like glass eyes. Red eye removal 17/04/2008
  • 21. False positives 21/26 One of the biggest issue in red eye removal are false p positives in the detection phase. p Unwanted corrections are much less desirable than missing ones ones. Red eye removal 17/04/2008
  • 22. Unnatural corrections 22/26 • Unnatural corrections are another important issue. • The most common ones are: • Partial correction: only a Partial correction portion of the red pupil has f been corrected; • Noisy correction: the presence of heavy noise or jpeg compression can introduce false red pixels around the pupil and Noisy correction thus a strange correction is made over the iris; • Wrong luminance correction: in this case the disk has been correctly found but the correction is unnatural due to Wrong luminance wrong luminance distribution. correction Red eye removal 17/04/2008
  • 23. Red Eye removal examples 23/26 StopRedeyes® AutoRemover® Red eye removal 17/04/2008
  • 24. Red Eye removal examples 24/26 StopRedeyes® AutoRemover® Red eye removal 17/04/2008
  • 25. Red Eye removal examples 25/26 StopRedeyes® AutoRemover® Red eye removal 17/04/2008
  • 26. References 26/26 • J.Y. Hardeberg, “Red Eye Removal using Digital Color Image Processing”, Conexant Systems, Inc. , Redmond, Washington, USA • M. Gaubatz, R. Ulichney, “Automatic Red-Eye Detection and Correction”, HP Lab, Cornell University, University ICIP 2002 • S. Ioffe, “Red Eye Detection With Machine Learning”, Fujifilm Software, California, ICIP 2003. • F Gasparini, R. Schettini “Automatic Redeye Removal for Smart Enhancement of Photos of F. Gasparini R Schettini, Automatic Unknown Origin”, DISCO University of Milano-Bicocca, VIS 2005. • H. Luo, J. Yen, D. Tretter, “An Efficient Redeye Detection and Correction Algorithm”, HP labs, Palo Alto, California, ICPR 2004. • A. Patti, K. Konstantinides, D. Tretter, Q. Lin, “Automatic Digital Redeye Reduction”, HP Labs, Palo Alto California, ICIP 1998. • L. Zhang, Y. Sun, M. Li, H. Zhang, “Automated Red-Eye Detection and Correction in Digital Photographs”, Photographs” Microsoft Research Asia China ICIP 2004 Asia, China, • B. Smolka, K. Czubin, J.Y. Hardeberg, K.N. Palataniotis, M. Szczepanski, K. Wojciechowski, “Towars Automatic Redeye Effect Removal”, Pattern Recognition Letter 2003. • J S Schildkraut R T Gray “A Fully Automatic Redeye Detection and Correction Algorithm”, J.S. Schildkraut, R. T. Gray, A Algorithm Kodak Company, NY, ICIP 2002. • R. Schettini, F. Gasparini, F. Chazli, ”A Modular Procedure for Automatic Redeye Correction in Digital Photos”, DISCO University of Milano-Bicocca, SPIE 2004 • PETSCHNIGG, G., AGRAWALA, M., HOPPE, H., SZELISKI, R., COHEN, M., and TOYAMA, K. 2004. Digital photography with flash and no-flash image pairs. ACM Transactions on Graphics 23, 3 (Aug.), 664–672. Red eye removal 17/04/2008