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[G4]image deblurring, seeing the invisible
2   CG Lab., POSTECH
Blur?

 Degrade image/video quality
 Shot/record under dim light circumstance
    Long exposure!




                         3                   CG Lab., POSTECH
Blur

 Various kinds of blur




       Camera shake (Camera motion blur)       Object movements (Object motion blur)




           Out of focus (Defocus blur)              Others (Wind, vibration, etc.)



                                           4                          CG Lab., POSTECH
Deblurring?

 Remove blur and restore a sharp image




      Input blurred image            Deblurring result

                            5                      CG Lab., POSTECH
Importance

 Image/video in our daily lives
    But, sometimes a retake is difficult
 Strong demand for high quality
    E.g. CCTV, car’s black box, medical imaging




                               6                   CG Lab., POSTECH
Contents

 Fast Motion Deblurring
    Sunghyun Cho and Seungyong Lee (POSTECH)
    ACM SIGGRAPH Asia 2009


 Text Image Deblurring Using Text-Specific Properties
    Hojin Cho (POSTECH), Jue Wang (Adobe), and Seungyong
     Lee (POSTECH)
    ECCV 2012 (to appear)




                            7                    CG Lab., POSTECH
Sunghyun Cho     Seungyong Lee

          POSTECH

    ACM SIGGRAPH Asia 2009
Motion blur

 Camera jitters




                   Latent sharp image
                      Blurred image


                           9            CG Lab., POSTECH
Image formation model

 Convolution
   Motion blur kernel
       Trace of a sensor




   Blurred image            Latent sharp image          Blur kernel

                                          *      : convolution operator


                            10                          CG Lab., POSTECH
Deblurring

 Non-blind deconvolution
   Ill-posed (Due to the loss of information caused by motion blur)




                Blurred image         Latent image      PSF
 Blind deconvolution
   Severely ill-posed




                Blurred image         Latent image      PSF
                                 11                           CG Lab., POSTECH
Blind deconvolution
                               Possible solutions
 Severely ill-posed problem
    No unique solution




          Blurred image




                          12                        CG Lab., POSTECH
Related work

 Parametric kernels
    Ex) 1D linear motion blur
    [Yitzhakey et al. 1998], [Rav-Acha and Peleg 2005], [Cho et
     al. 2007], [Money and Kang 2008], …




      Blurred image            Latent sharp image      PSF



                              13                     CG Lab., POSTECH
Related work

 More complex motion blur
   [Fergus et al. 2006], [Jia 2007], [Shan et al. 2008]
   Excessive amount of computation




      Blurred image            Latent sharp image          PSF



                               14                      CG Lab., POSTECH
Motivation

 Computation time  Important for practical
  purpose
 Previous methods are slow
                          [Fergus et al. 2006] took 1 hr 25 min.
                          [Shan et al. 2008] took 4 min 48 sec.


                          Our method took 5.766 sec. in CPU
                                      and 0.734 sec. using GPU accel.
  Image size: 640 x 480
   kernel size: 25 x 25




                              15                          CG Lab., POSTECH
Contributions

 Fast motion deblurring
    Only a few sec.
    Fast latent image estimation
    Fast blur kernel estimation
    40x ~ 60x faster than [Shan et al. 2008]

    GPU acceleration
    600x ~ 800x faster than [Shan et al. 2008]




                              16                  CG Lab., POSTECH
Motion deblurring: Common framework

 Iteratively solve
   1. Estimate a PSF



             Blurred image   Latent image   PSF

   2. Estimate a latent sharp image using a complex image
      prior


             Blurred image   Latent image   PSF



                               17                  CG Lab., POSTECH
Motion deblurring: Common framework

 Blur model
                              B  L* K  N



      Blurred image B        Latent image L       Blur kernel K   Noise N


 Energy function
             f ( L, K )  B  L * K  q( L)  r ( K )
                                              2


               * : convolution operator
               q(L), r(K) : regularization terms or priors for L, K


                                        18                              CG Lab., POSTECH
Motion deblurring: Common framework
Blurred image




                Latent image estimation                     Kernel estimation
                L '  arg min B  L * K  q( L)
                                      2
                                                       K '  arg min B  L * K  r ( K )
                                                                               2

                        L                                      K




                                                                                    Deblurred result




                                                  19                                CG Lab., POSTECH
Latent image estimation:                          Blurred image   Latent image
                                                                   estimation
                                                                                       Kernel
                                                                                     estimation
                                                                                                  Deblurred result




Analysis of prev. methods
 Two important properties
   Restoration of strong edges
      Inspecting around strong edges,
       we can find a blur kernel
   Noise suppression in
    smooth regions
      Avoids the effect of noise            Blurry input                          Latent image
                                                                                   estimation of
       on kernel estimation
                                                                                 [Shan et al. 2008]



            Edge of a blurred image        Edge of a sharp image




                                      20                                           CG Lab., POSTECH
Latent image estimation:                     Blurred image   Latent image
                                                              estimation
                                                                                  Kernel
                                                                                estimation
                                                                                             Deblurred result




Analysis of prev. methods
 Two important properties
   Restoration of strong edges
      Inspecting around strong edges,
       we can find a blur kernel
   Noise suppression in
    smooth regions
      Avoids the effect of noise        Blurry input                         Latent image
                                                                              estimation of
       on kernel estimation
                                                                            [Shan et al. 2008]




                                    21                                        CG Lab., POSTECH
Latent image estimation:                       Blurred image   Latent image
                                                                estimation
                                                                                    Kernel
                                                                                  estimation
                                                                                               Deblurred result




Analysis of prev. methods
 Two important properties
    Restoration of strong edges
        Inspecting around strong edges,
         we can find a blur kernel
    Noise suppression in
     smooth regions
        Avoids the effect of noise        Blurry input                         Latent image
                                                                                estimation of
         on kernel estimation
                                                                              [Shan et al. 2008]
 Computationally expensive priors for q(L)
    L '  arg min B  L * K  q( L)
                          2

            L




                                      22                                        CG Lab., POSTECH
Latent image estimation:                              Blurred image      Latent image
                                                                          estimation
                                                                                           Kernel
                                                                                         estimation
                                                                                                      Deblurred result




Basic idea for acceleration
We divide…                  Latent image estimation




    Simple Deconvolution                                              Prediction
     Removes blur quickly                          Restores strong edges
      Low-quality results                              Removes noise
                                                Simple image processing tools




                                      23                                                CG Lab., POSTECH
Latent image estimation:                 Blurred image      Latent image
                                                             estimation
                                                                              Kernel
                                                                            estimation
                                                                                         Deblurred result




Basic idea for acceleration




      Simple Deconvolution        Prediction




        Current kernel                                   Updated kernel


                             24                                            CG Lab., POSTECH
Deblurring process
 Blurred image




    Prediction        Kernel estimation        Deconvolution        Final
                                                                deconvolution




                                                               Deblurred result
* Deconvolution + prediction = latent image estimation




                                          25                      CG Lab., POSTECH
Results




      Blurry input                            Our result                  Blur
                                                                         kernel

                                             Processing time   Processing time
             Image size   Blur kernel size
                                                  (CPU)            (GPU)
             1024 x 768       49 x 47          18.656 sec.       2.125 sec.


26                                26                           CG Lab., POSTECH
Results




          Blurry input                          Our result           Blur kernel

                                                   Processing time   Processing time
                Image size   Blur kernel size
                                                        (CPU)            (GPU)
                972 x 966        65 x 93             18.813 sec.       5.766 sec.


27                                   27                              CG Lab., POSTECH
Results




      Blurry input                             Our result              Blur kernel


                                              Processing time   Processing time
              Image size   Blur kernel size
                                                   (CPU)            (GPU)
              858 x 558        61 x 43          8.969 sec.        0.703 sec.


28                                 28                           CG Lab., POSTECH
Comparison (quality)



                                                                Blurry input




     [Yuan et al. 2007]                [Shan et al. 2008]   our method
* This method uses two input images.




                                              29               CG Lab., POSTECH
Implementation Details

 Requirements?
     Vector/matrix calculus
     Fast Fourier transform (FFT)
     Image filters (bilateral, shock)
     Convex optimization
        Solver: Conjugate gradient method




                                 30          CG Lab., POSTECH
Hojin Cho1 Jue Wang2 Seungyong Lee1
              1POSTECH     2Adobe


12th European Conference on Computer Vision (ECCV)
                    (to appear)
Introduction

 Common blur model
                          b= k* l +n




                          ∗                   =

    Latent image l   Convolution                   Blurred image b
                      operator          Kernel k



                                   32                     CG Lab., POSTECH
Introduction

 Image deblurring




     Synthetic blurred image        Deblurring result of [Levin et al. CVPR 2011]



                               33                               CG Lab., POSTECH
Introduction

 Image deblurring




     Synthetic blurred image        Deblurring result of [Levin et al. CVPR 2011]



                               34                               CG Lab., POSTECH
Difficulties

   Why is handling text difficult?
         (1) Different statistics against natural images
         (2) Spatial distribution of text is highly regulated




                     Log-scale        Gradient                          Log-scale        Gradient
Natural image                                         Text image
                gradient histogram   magnitude                     gradient histogram   magnitude



        Directly applying a natural image deblurring method to a text image
                         will result in an erroneous result!

                                                 35                              CG Lab., POSTECH
Our Approach

 Exploit desired “general” properties of text images
 Introduce a new optimization framework to
  incorporate the text properties in deblurring
    Utilizing domain-specific properties




       Real blurred image                   Our result

                              36                         CG Lab., POSTECH
Desired Properties of Latent Text Images – 1/3

 Text boundary
   High contrasts along the texts’ boundary




                             37                CG Lab., POSTECH
Desired Properties of Latent Text Images – 2/3

 Character color
    Near-uniform color inside characters




    POSTECH
                 1) Near-uniform color
                 2) Gradients are close to zero




                                   38             CG Lab., POSTECH
Desired Properties of Latent Text Images – 3/3

 Background gradients
   Too restrictive to assume a single background color (e.g.,
    advertisements or posters)
   Similar to the natural image




                              39                     CG Lab., POSTECH
Optimization Framework                                               Blur model

                                                                   b = k* l + n
 Deblurring problem

   arg min b  k * l  l (l )   k (k )
                               2

      l ,k


       l (l )     : A prior for the latent image

        k (k )    : A prior for the motion blur kernel




   arg min b  k * l  l (l )   k (k )   a (a )   l  a
                               2                                       2

     l ,k ,a


       a (a)      : prior for the auxiliary (reference) image a

               a   : reflects the text properties

                                                    40                     CG Lab., POSTECH
Deblurring Algorithm

                                          Kernel Estimation

                                  Estimating l

     Input                                                                   Final Deconvolution
                         Computing l         Computing a      Estimating k
 Blurred image b                                                                Result image l




                   • Estimating l
                        Restore sharp edges along the boundaries of texts
                          considering the text properties 1 and 2
                   • Estimating k
                        Compute the blur kernel (camera’s trajectory)
                   • Final deconvolution
                        Restores texts and background using k & b

                                                    41                            CG Lab., POSTECH
Deblurring Algorithm

                                      Kernel Estimation

                              Estimating l

       Input                                                              Final Deconvolution
                     Computing l         Computing a       Estimating k
   Blurred image b                                                           Result image l




                                                                                     Blur kernel k
                                                 Computing l


Blurred image b

                                                                                    Result image l
                                                 Computing a

                                                42                             CG Lab., POSTECH
Kernel Estimation




Algorithm Details
                                                                                        Estimating l

                                                             Input                                                                  Final Deconvolution
                                                                          Computing l                  Computing a   Estimating k
                                                          Blurred image                                                                Result image




 Step 1: Computing l
   a reflects the text properties
   The text properties soak into l by solving:

                       arg min b  k * l  l (l )   l  a
                                                 2                                       2

                               l

                         a plays a role as a guidance(reference) image




                          1
                                          (k ) (b)   (a)       
                  l                                             
                                  (k ) (k )   (1)  l () () 

       Can be solved efficiently using FFTs and component-wise operators(multiplication/division)




                                                     43                                                              CG Lab., POSTECH
Kernel Estimation




        Algorithm Details
                                                                                                          Estimating l

                                                                               Input                                                                  Final Deconvolution
                                                                                            Computing l                  Computing a   Estimating k
                                                                            Blurred image                                                                Result image




         Step 2: Computing a
                Initialize a by refining l computed from Step 1
                       Reduce ringing artifacts and noise caused by simple deconvolution
                        through L0 gradient minimization




                        Bilateral filter                        Total variation (TV)                             L0 gradient minimization

                                The result of L0 gradient minimization can provide a good initial for a
                           [Property 1] The boundaries of texts have large gradient values
                           [Property 2] Gradients inside characters are close to zero
                           Noise and ringing artifacts generally have small magnitudes, thus being reduced


* The figures are provided in [Xu et al., SIGGRAPH Asia 2011]          44                                                              CG Lab., POSTECH
Kernel Estimation




Algorithm Details
                                                                                          Estimating l

                                                           Input                                                                      Final Deconvolution
                                                                            Computing l                  Computing a   Estimating k
                                                        Blurred image                                                                    Result image




 Step 2: Computing a
   With the initial a, we iteratively solve:
                                   arg min  a (a)   l  a
                                                                        2

                                       a

       a (a)     : a cost function which is minimized when a satisfies the text image properties

      l a
              2
                  : a data term to penalize non-similarity between a and l




                                                             0             if ai =aiP
                          For each pixel at i,   a (ai )  
                                                            d MAX      otherwise
         aP     : an ideal image satisfying the text image properties 1 & 2 completely.


          After minimizing the equation,
          a will reflect the text image properties while preserving the similarity against l


                                                   45                                                                  CG Lab., POSTECH
Kernel Estimation




Algorithm Details
                                                                                Estimating l

                                                     Input                                                                  Final Deconvolution
                                                                  Computing l                  Computing a   Estimating k
                                                  Blurred image                                                                Result image




   Computing the ideal image aP:
      Detect text regions from l using the stroke width transform (SWT)




     Typical stroke                Searching in the direction                                  Detected stroke width
                                    of the gradient at edges

      For detected text regions (connected strokes), we force the pixels
       to have the same color value
     Through the SWT and re-coloring, the ideal image satisfies the following properties:
          [Property 1] The large gradient around the boundaries of text characters
          [Property 2] Colors are near uniform inside text characters

                                             46                                                              CG Lab., POSTECH
Kernel Estimation




Algorithm Details
                                                                                       Estimating l

                                                           Input                                                                           Final Deconvolution
                                                                         Computing l                  Computing a           Estimating k
                                                        Blurred image                                                                         Result image




                                                                                             0                if ai =aiP
               arg min  a (a)   l  a
                                               2
                                                                where     a (ai )  
                   a                                                                        d MAX           otherwise




      a (a)    : a cost function which is minimized when a satisfies the text image properties

     l a
            2
                : a data term to penalize non-similarity between a and l




                                           Solution

                                                a P
                                                       if d MAX   li  aiP
                                                                                          2

                    For each pixel at i,   ai   i
                                            *

                                                 li
                                                                  otherwise




                                                   47                                                                       CG Lab., POSTECH
Kernel Estimation




Algorithm Details
                                                                                    Estimating l

                                                     Input                                                                      Final Deconvolution
                                                                      Computing l                  Computing a   Estimating k
                                                  Blurred image                                                                    Result image




 Computing k
   Adopted from [Shan et al. SIGGRAPH 2008] and [Cho and
    Lee, SIGGRAPH Asia 2009]

                              ( ) *b  k * *a   k ,
                                                                  2
                  E (k ) 
                                                                                        2
                                      *

                              
                             *




                               { x ,  y ,  xx ,  xy ,  yy }


   The auxiliary image a is used instead of the latent image l
      a contains less ringing artifacts and noise due to the L0 gradient
       minimization



                                            48                                                                   CG Lab., POSTECH
Kernel Estimation




Algorithm Details
                                                                                         Estimating l

                                                        Input                                                                              Final Deconvolution
                                                                           Computing l                  Computing a         Estimating k
                                                     Blurred image                                                                            Result image l




 Final deconvolution
    Given the estimate kernel and the input blurred image, we
     minimize:
                                        (1)                          (2)                                   (3)
          E (l )  b  k * l  1  li  2  li  ai  3  li
                               2                2                                2                                    0.8

                                     iT                    iT                                    iT


                    T  {i | a (ai )  0} :   Set of pixel indices for text regions



        (1): [Property 1 & 2] Gradient values inside each character should be close to zero

        (2): [Property 1 & 2] Each character has a near-uniform color

        (3): [Property 3] Gradient values of background are sparse




                                                49                                                                          CG Lab., POSTECH
Results

 Synthetic example




 Deblurring result of [Levin et al. CVPR 2011]        Our result




                                                 50                CG Lab., POSTECH
Original image




  Synthetic blurred image




Fast motion deblurring result




         Our result
                                51
Results on Real Photographs (1/4)




                  Blurred image




                   Our result




                                    52
Results on Real Photographs (2/4)




      Blurred image                 Our result




                                                 53
Results on Real Photographs (3/4)




        Blurred image               Our result



                                                 54
Results on Real Photographs (4/4)




                        Blurred image




             Our result (very large blur: 105x105)   55
Comparison – Real Images




Blurred image     Fergus et al.           Shan et al.       Cho and Lee
                SIGGRAPH 2006          SIGGRAPH 2008    SIGGRAPH Asia 2009




 Xu and Jia       Levin et al.           Chen et al.       Our method
 ECCV 2010        CVPR 2011       56     CVPR 2011       CG Lab., POSTECH
Comparison – Real Images




Blurred image     Fergus et al.           Shan et al.       Cho and Lee
                SIGGRAPH 2006          SIGGRAPH 2008    SIGGRAPH Asia 2009




 Xu and Jia       Levin et al.           Chen et al.       Our method
 ECCV 2010        CVPR 2011              CVPR 2011
                                  57                     CG Lab., POSTECH
Comparison – Real Images




          Blurred image                         Fergus et al.                         Shan et al.                          Cho and Lee
                                              SIGGRAPH 2006                        SIGGRAPH 2008                       SIGGRAPH Asia 2009




                                                                                     Not available




            Xu and Jia                           Levin et al.                         Chen et al.                         Our method
            ECCV 2010                            CVPR 2011                            CVPR 2011
* Since Chen et al.’s approach requires training, re-implementing it is impossible without having the training data.
Comparison – Real Images




          Blurred image                         Fergus et al.                         Shan et al.                          Cho and Lee
                                              SIGGRAPH 2006                        SIGGRAPH 2008                       SIGGRAPH Asia 2009




                                                                                     Not available




            Xu and Jia                           Levin et al.                         Chen et al.                         Our method
            ECCV 2010                            CVPR 2011                            CVPR 2011
* Since Chen et al.’s approach requires training, re-implementing it is impossible without having the training data.
Comparison – Real Images



                                                                                     Not available




          Blurred image                         Fergus et al.                         Shan et al.                          Cho and Lee
                                              SIGGRAPH 2006                        SIGGRAPH 2008                       SIGGRAPH Asia 2009




                                                Not available                        Not available




            Xu and Jia                           Levin et al.                         Chen et al.                         Our method
            ECCV 2010                            CVPR 2011                            CVPR 2011
* The methods of Shan et al. and Levin et al. could not handle this image.
* Since Chen et al.’s approach requires training, re-implementing it is impossible without having the training data.
Conclusion

 Previous natural image deblurring methods do not work well
  for text images
    Due to the lack of consideration of text-specific properties
 We analyzed the desired properties for latent text images
 We proposed a novel text image deblurring algorithm which
  explicitly incorporates the text-specific properties into the
  optimization framework




                                    61                          CG Lab., POSTECH
Applications




               62   CG Lab., POSTECH
Q&A
http://cg.postech.ac.kr

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[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
 
[224]네이버 검색과 개인화
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[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
 
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
 
[213] Fashion Visual Search
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[232] TensorRT를 활용한 딥러닝 Inference 최적화
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[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지
[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지
[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지
 
[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터
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[223]기계독해 QA: 검색인가, NLP인가?
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[G4]image deblurring, seeing the invisible

  • 2. 2 CG Lab., POSTECH
  • 3. Blur?  Degrade image/video quality  Shot/record under dim light circumstance  Long exposure! 3 CG Lab., POSTECH
  • 4. Blur  Various kinds of blur Camera shake (Camera motion blur) Object movements (Object motion blur) Out of focus (Defocus blur) Others (Wind, vibration, etc.) 4 CG Lab., POSTECH
  • 5. Deblurring?  Remove blur and restore a sharp image Input blurred image Deblurring result 5 CG Lab., POSTECH
  • 6. Importance  Image/video in our daily lives  But, sometimes a retake is difficult  Strong demand for high quality  E.g. CCTV, car’s black box, medical imaging 6 CG Lab., POSTECH
  • 7. Contents  Fast Motion Deblurring  Sunghyun Cho and Seungyong Lee (POSTECH)  ACM SIGGRAPH Asia 2009  Text Image Deblurring Using Text-Specific Properties  Hojin Cho (POSTECH), Jue Wang (Adobe), and Seungyong Lee (POSTECH)  ECCV 2012 (to appear) 7 CG Lab., POSTECH
  • 8. Sunghyun Cho Seungyong Lee POSTECH ACM SIGGRAPH Asia 2009
  • 9. Motion blur  Camera jitters Latent sharp image Blurred image 9 CG Lab., POSTECH
  • 10. Image formation model  Convolution  Motion blur kernel  Trace of a sensor Blurred image Latent sharp image Blur kernel * : convolution operator 10 CG Lab., POSTECH
  • 11. Deblurring  Non-blind deconvolution  Ill-posed (Due to the loss of information caused by motion blur) Blurred image Latent image PSF  Blind deconvolution  Severely ill-posed Blurred image Latent image PSF 11 CG Lab., POSTECH
  • 12. Blind deconvolution Possible solutions  Severely ill-posed problem  No unique solution Blurred image 12 CG Lab., POSTECH
  • 13. Related work  Parametric kernels  Ex) 1D linear motion blur  [Yitzhakey et al. 1998], [Rav-Acha and Peleg 2005], [Cho et al. 2007], [Money and Kang 2008], … Blurred image Latent sharp image PSF 13 CG Lab., POSTECH
  • 14. Related work  More complex motion blur  [Fergus et al. 2006], [Jia 2007], [Shan et al. 2008]  Excessive amount of computation Blurred image Latent sharp image PSF 14 CG Lab., POSTECH
  • 15. Motivation  Computation time  Important for practical purpose  Previous methods are slow [Fergus et al. 2006] took 1 hr 25 min. [Shan et al. 2008] took 4 min 48 sec. Our method took 5.766 sec. in CPU and 0.734 sec. using GPU accel. Image size: 640 x 480 kernel size: 25 x 25 15 CG Lab., POSTECH
  • 16. Contributions  Fast motion deblurring  Only a few sec.  Fast latent image estimation  Fast blur kernel estimation  40x ~ 60x faster than [Shan et al. 2008]  GPU acceleration  600x ~ 800x faster than [Shan et al. 2008] 16 CG Lab., POSTECH
  • 17. Motion deblurring: Common framework  Iteratively solve 1. Estimate a PSF Blurred image Latent image PSF 2. Estimate a latent sharp image using a complex image prior Blurred image Latent image PSF 17 CG Lab., POSTECH
  • 18. Motion deblurring: Common framework  Blur model B  L* K  N Blurred image B Latent image L Blur kernel K Noise N  Energy function f ( L, K )  B  L * K  q( L)  r ( K ) 2 * : convolution operator q(L), r(K) : regularization terms or priors for L, K 18 CG Lab., POSTECH
  • 19. Motion deblurring: Common framework Blurred image Latent image estimation Kernel estimation L '  arg min B  L * K  q( L) 2 K '  arg min B  L * K  r ( K ) 2 L K Deblurred result 19 CG Lab., POSTECH
  • 20. Latent image estimation: Blurred image Latent image estimation Kernel estimation Deblurred result Analysis of prev. methods  Two important properties  Restoration of strong edges  Inspecting around strong edges, we can find a blur kernel  Noise suppression in smooth regions  Avoids the effect of noise Blurry input Latent image estimation of on kernel estimation [Shan et al. 2008] Edge of a blurred image Edge of a sharp image 20 CG Lab., POSTECH
  • 21. Latent image estimation: Blurred image Latent image estimation Kernel estimation Deblurred result Analysis of prev. methods  Two important properties  Restoration of strong edges  Inspecting around strong edges, we can find a blur kernel  Noise suppression in smooth regions  Avoids the effect of noise Blurry input Latent image estimation of on kernel estimation [Shan et al. 2008] 21 CG Lab., POSTECH
  • 22. Latent image estimation: Blurred image Latent image estimation Kernel estimation Deblurred result Analysis of prev. methods  Two important properties  Restoration of strong edges  Inspecting around strong edges, we can find a blur kernel  Noise suppression in smooth regions  Avoids the effect of noise Blurry input Latent image estimation of on kernel estimation [Shan et al. 2008]  Computationally expensive priors for q(L) L '  arg min B  L * K  q( L) 2 L 22 CG Lab., POSTECH
  • 23. Latent image estimation: Blurred image Latent image estimation Kernel estimation Deblurred result Basic idea for acceleration We divide… Latent image estimation Simple Deconvolution Prediction Removes blur quickly Restores strong edges Low-quality results Removes noise Simple image processing tools 23 CG Lab., POSTECH
  • 24. Latent image estimation: Blurred image Latent image estimation Kernel estimation Deblurred result Basic idea for acceleration Simple Deconvolution Prediction Current kernel Updated kernel 24 CG Lab., POSTECH
  • 25. Deblurring process Blurred image Prediction Kernel estimation Deconvolution Final deconvolution Deblurred result * Deconvolution + prediction = latent image estimation 25 CG Lab., POSTECH
  • 26. Results Blurry input Our result Blur kernel Processing time Processing time Image size Blur kernel size (CPU) (GPU) 1024 x 768 49 x 47 18.656 sec. 2.125 sec. 26 26 CG Lab., POSTECH
  • 27. Results Blurry input Our result Blur kernel Processing time Processing time Image size Blur kernel size (CPU) (GPU) 972 x 966 65 x 93 18.813 sec. 5.766 sec. 27 27 CG Lab., POSTECH
  • 28. Results Blurry input Our result Blur kernel Processing time Processing time Image size Blur kernel size (CPU) (GPU) 858 x 558 61 x 43 8.969 sec. 0.703 sec. 28 28 CG Lab., POSTECH
  • 29. Comparison (quality) Blurry input [Yuan et al. 2007] [Shan et al. 2008] our method * This method uses two input images. 29 CG Lab., POSTECH
  • 30. Implementation Details  Requirements?  Vector/matrix calculus  Fast Fourier transform (FFT)  Image filters (bilateral, shock)  Convex optimization  Solver: Conjugate gradient method 30 CG Lab., POSTECH
  • 31. Hojin Cho1 Jue Wang2 Seungyong Lee1 1POSTECH 2Adobe 12th European Conference on Computer Vision (ECCV) (to appear)
  • 32. Introduction  Common blur model b= k* l +n ∗ = Latent image l Convolution Blurred image b operator Kernel k 32 CG Lab., POSTECH
  • 33. Introduction  Image deblurring Synthetic blurred image Deblurring result of [Levin et al. CVPR 2011] 33 CG Lab., POSTECH
  • 34. Introduction  Image deblurring Synthetic blurred image Deblurring result of [Levin et al. CVPR 2011] 34 CG Lab., POSTECH
  • 35. Difficulties  Why is handling text difficult?  (1) Different statistics against natural images  (2) Spatial distribution of text is highly regulated Log-scale Gradient Log-scale Gradient Natural image Text image gradient histogram magnitude gradient histogram magnitude Directly applying a natural image deblurring method to a text image will result in an erroneous result! 35 CG Lab., POSTECH
  • 36. Our Approach  Exploit desired “general” properties of text images  Introduce a new optimization framework to incorporate the text properties in deblurring  Utilizing domain-specific properties Real blurred image Our result 36 CG Lab., POSTECH
  • 37. Desired Properties of Latent Text Images – 1/3  Text boundary  High contrasts along the texts’ boundary 37 CG Lab., POSTECH
  • 38. Desired Properties of Latent Text Images – 2/3  Character color  Near-uniform color inside characters POSTECH 1) Near-uniform color 2) Gradients are close to zero 38 CG Lab., POSTECH
  • 39. Desired Properties of Latent Text Images – 3/3  Background gradients  Too restrictive to assume a single background color (e.g., advertisements or posters)  Similar to the natural image 39 CG Lab., POSTECH
  • 40. Optimization Framework Blur model b = k* l + n  Deblurring problem arg min b  k * l  l (l )   k (k ) 2 l ,k l (l ) : A prior for the latent image  k (k ) : A prior for the motion blur kernel arg min b  k * l  l (l )   k (k )   a (a )   l  a 2 2 l ,k ,a  a (a) : prior for the auxiliary (reference) image a a : reflects the text properties 40 CG Lab., POSTECH
  • 41. Deblurring Algorithm Kernel Estimation Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image b Result image l • Estimating l  Restore sharp edges along the boundaries of texts considering the text properties 1 and 2 • Estimating k  Compute the blur kernel (camera’s trajectory) • Final deconvolution  Restores texts and background using k & b 41 CG Lab., POSTECH
  • 42. Deblurring Algorithm Kernel Estimation Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image b Result image l Blur kernel k Computing l Blurred image b Result image l Computing a 42 CG Lab., POSTECH
  • 43. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image  Step 1: Computing l  a reflects the text properties  The text properties soak into l by solving: arg min b  k * l  l (l )   l  a 2 2 l a plays a role as a guidance(reference) image 1  (k ) (b)   (a)  l    (k ) (k )   (1)  l () ()  Can be solved efficiently using FFTs and component-wise operators(multiplication/division) 43 CG Lab., POSTECH
  • 44. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image  Step 2: Computing a  Initialize a by refining l computed from Step 1  Reduce ringing artifacts and noise caused by simple deconvolution through L0 gradient minimization Bilateral filter Total variation (TV) L0 gradient minimization The result of L0 gradient minimization can provide a good initial for a  [Property 1] The boundaries of texts have large gradient values  [Property 2] Gradients inside characters are close to zero  Noise and ringing artifacts generally have small magnitudes, thus being reduced * The figures are provided in [Xu et al., SIGGRAPH Asia 2011] 44 CG Lab., POSTECH
  • 45. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image  Step 2: Computing a  With the initial a, we iteratively solve: arg min  a (a)   l  a 2 a  a (a) : a cost function which is minimized when a satisfies the text image properties l a 2 : a data term to penalize non-similarity between a and l  0 if ai =aiP For each pixel at i, a (ai )   d MAX otherwise aP : an ideal image satisfying the text image properties 1 & 2 completely. After minimizing the equation, a will reflect the text image properties while preserving the similarity against l 45 CG Lab., POSTECH
  • 46. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image  Computing the ideal image aP:  Detect text regions from l using the stroke width transform (SWT) Typical stroke Searching in the direction Detected stroke width of the gradient at edges  For detected text regions (connected strokes), we force the pixels to have the same color value Through the SWT and re-coloring, the ideal image satisfies the following properties:  [Property 1] The large gradient around the boundaries of text characters  [Property 2] Colors are near uniform inside text characters 46 CG Lab., POSTECH
  • 47. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image  0 if ai =aiP arg min  a (a)   l  a 2 where a (ai )   a d MAX otherwise  a (a) : a cost function which is minimized when a satisfies the text image properties l a 2 : a data term to penalize non-similarity between a and l Solution a P  if d MAX   li  aiP 2 For each pixel at i, ai   i *  li  otherwise 47 CG Lab., POSTECH
  • 48. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image  Computing k  Adopted from [Shan et al. SIGGRAPH 2008] and [Cho and Lee, SIGGRAPH Asia 2009]  ( ) *b  k * *a   k , 2 E (k )  2 *   *   { x ,  y ,  xx ,  xy ,  yy }  The auxiliary image a is used instead of the latent image l  a contains less ringing artifacts and noise due to the L0 gradient minimization 48 CG Lab., POSTECH
  • 49. Kernel Estimation Algorithm Details Estimating l Input Final Deconvolution Computing l Computing a Estimating k Blurred image Result image l  Final deconvolution  Given the estimate kernel and the input blurred image, we minimize: (1) (2) (3) E (l )  b  k * l  1  li  2  li  ai  3  li 2 2 2 0.8 iT iT iT T  {i | a (ai )  0} : Set of pixel indices for text regions (1): [Property 1 & 2] Gradient values inside each character should be close to zero (2): [Property 1 & 2] Each character has a near-uniform color (3): [Property 3] Gradient values of background are sparse 49 CG Lab., POSTECH
  • 50. Results  Synthetic example Deblurring result of [Levin et al. CVPR 2011] Our result 50 CG Lab., POSTECH
  • 51. Original image Synthetic blurred image Fast motion deblurring result Our result 51
  • 52. Results on Real Photographs (1/4) Blurred image Our result 52
  • 53. Results on Real Photographs (2/4) Blurred image Our result 53
  • 54. Results on Real Photographs (3/4) Blurred image Our result 54
  • 55. Results on Real Photographs (4/4) Blurred image Our result (very large blur: 105x105) 55
  • 56. Comparison – Real Images Blurred image Fergus et al. Shan et al. Cho and Lee SIGGRAPH 2006 SIGGRAPH 2008 SIGGRAPH Asia 2009 Xu and Jia Levin et al. Chen et al. Our method ECCV 2010 CVPR 2011 56 CVPR 2011 CG Lab., POSTECH
  • 57. Comparison – Real Images Blurred image Fergus et al. Shan et al. Cho and Lee SIGGRAPH 2006 SIGGRAPH 2008 SIGGRAPH Asia 2009 Xu and Jia Levin et al. Chen et al. Our method ECCV 2010 CVPR 2011 CVPR 2011 57 CG Lab., POSTECH
  • 58. Comparison – Real Images Blurred image Fergus et al. Shan et al. Cho and Lee SIGGRAPH 2006 SIGGRAPH 2008 SIGGRAPH Asia 2009 Not available Xu and Jia Levin et al. Chen et al. Our method ECCV 2010 CVPR 2011 CVPR 2011 * Since Chen et al.’s approach requires training, re-implementing it is impossible without having the training data.
  • 59. Comparison – Real Images Blurred image Fergus et al. Shan et al. Cho and Lee SIGGRAPH 2006 SIGGRAPH 2008 SIGGRAPH Asia 2009 Not available Xu and Jia Levin et al. Chen et al. Our method ECCV 2010 CVPR 2011 CVPR 2011 * Since Chen et al.’s approach requires training, re-implementing it is impossible without having the training data.
  • 60. Comparison – Real Images Not available Blurred image Fergus et al. Shan et al. Cho and Lee SIGGRAPH 2006 SIGGRAPH 2008 SIGGRAPH Asia 2009 Not available Not available Xu and Jia Levin et al. Chen et al. Our method ECCV 2010 CVPR 2011 CVPR 2011 * The methods of Shan et al. and Levin et al. could not handle this image. * Since Chen et al.’s approach requires training, re-implementing it is impossible without having the training data.
  • 61. Conclusion  Previous natural image deblurring methods do not work well for text images  Due to the lack of consideration of text-specific properties  We analyzed the desired properties for latent text images  We proposed a novel text image deblurring algorithm which explicitly incorporates the text-specific properties into the optimization framework 61 CG Lab., POSTECH
  • 62. Applications 62 CG Lab., POSTECH