Is there a way to detect if an image is blurry

Question

I was wondering if there is a way to determine if an image is blurry or not by analyzing the image data

User · Accepted Answer

Yes  it is  Compute the Fast Fourier Transform and analyse the result  The Fourier transform tells you which frequencies are present in the image  If there is a low amount of high frequencies  then the image is blurry   Defining the terms  low  and  high  is up to you   Edit   As stated in the comments  if you want a single float representing the blurryness of a given image  you have to work out a suitable metric    nikie s answer provide such a metric  Convolve the image with a Laplacian kernel      1 1 -4  1    1   And use a robust maximum metric on the output to get a number which you can use for thresholding  Try to avoid smoothing too much the images before computing the Laplacian  because you will only find out that a smoothed image is indeed blurry  -

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I tried solution based on Laplacian filter from this post  It didn t help me  So  I tried the solution from this post and it was good for my case  but is slow    import cv2  image   cv2 imread  test jpeg   height  width   image shape  2  gray   cv2 cvtColor image  cv2 COLOR BGR2GRAY   def px x  y       return int gray y  x    sum   0 for x in range width-1       for y in range height           sum    abs px x  y  - px x 1  y     Less blurred image has maximum sum value   You can also tune speed and accuracy by changing step  e g    this part  for x in range width - 1     you can replace with this one  for x in range 0  width - 1  10

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That s what I do in Opencv to detect focus quality in a region   Mat grad  int scale   1  int delta   0  int ddepth   CV 8U  Mat grad x  grad y  Mat abs grad x  abs grad y      Gradient X Sobel matFromSensor  grad x  ddepth  1  0  3  scale  delta  BORDER DEFAULT       Gradient Y Sobel matFromSensor  grad y  ddepth  0  1  3  scale  delta  BORDER DEFAULT   convertScaleAbs grad x  abs grad x   convertScaleAbs grad y  abs grad y   addWeighted abs grad x  0 5  abs grad y  0 5  0  grad   cv  Scalar mu  sigma  cv  meanStdDev grad     mean    mu    stdev   sigma   focusMeasure   mu val 0    mu val 0

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During some work with an auto-focus lens  I came across this very useful set of algorithms for detecting image focus  It s implemented in MATLAB  but most of the functions are quite easy to port to OpenCV with filter2D   It s basically a survey implementation of many focus measurement algorithms  If you want to read the original papers  references to the authors of the algorithms are provided in the code  The 2012 paper by Pertuz  et al  Analysis of focus measure operators for shape from focus  SFF  gives a great review of all of these measure as well as their performance  both in terms of speed and accuracy as applied to SFF    EDIT  Added MATLAB code just in case the link dies   function FM   fmeasure Image  Measure  ROI   This function measures the relative degree of focus of   an image  It may be invoked as        FM   fmeasure Image  Method  ROI     Where      Image   is a grayscale image and FM is the computed             focus value      Method  is the focus measure algorithm as a string              see  operators txt  for a list of focus              measure methods       ROI     Image ROI as a rectangle  xo yo width heigth               if an empty argument is passed  the whole             image is processed       Said Pertuz    Abr 2010   if  isempty ROI      Image   imcrop Image  ROI   end  WSize   15    Size of local window  only some operators   switch upper Measure      case  ACMO    Absolute Central Moment  Shirvaikar2004          if  isinteger Image   Image   im2uint8 Image           end         FM   AcMomentum Image        case  BREN    Brenner s  Santos97           M N    size Image           DH   Image          DV   Image          DH 1 M-2      diff Image 2 1           DV   1 N-2    diff Image 2 2           FM   max DH  DV                   FM   FM  2          FM   mean2 FM        case  CONT    Image contrast  Nanda2001          ImContrast   inline  sum abs x   -x 5               FM   nlfilter Image   3 3   ImContrast           FM   mean2 FM        case  CURV    Image Curvature  Helmli2001          if  isinteger Image   Image   im2uint8 Image           end         M1    -1 0 1 -1 0 1 -1 0 1           M2    1 0 1 1 0 1 1 0 1           P0   imfilter Image  M1   replicate    conv   6          P1   imfilter Image  M1    replicate    conv   6          P2   3 imfilter Image  M2   replicate    conv   10                 -imfilter Image  M2    replicate    conv   5          P3   -imfilter Image  M2   replicate    conv   5                  3 imfilter Image  M2   replicate    conv   10          FM   abs P0    abs P1    abs P2    abs P3           FM   mean2 FM        case  DCTE    DCT energy ratio  Shen2006          FM   nlfilter Image   8 8    DctRatio           FM   mean2 FM        case  DCTR    DCT reduced energy ratio  Lee2009          FM   nlfilter Image   8 8    ReRatio           FM   mean2 FM        case  GDER    Gaussian derivative  Geusebroek2000                  N   floor WSize 2           sig   N 2 5           x y    meshgrid -N N  -N N           G   exp - x  2 y  2   2 sig 2    2 pi sig           Gx   -x  G  sig 2  Gx   Gx sum Gx              Gy   -y  G  sig 2  Gy   Gy sum Gy              Rx   imfilter double Image   Gx   conv    replicate            Ry   imfilter double Image   Gy   conv    replicate            FM   Rx  2 Ry  2          FM   mean2 FM        case  GLVA    Graylevel variance  Krotkov86          FM   std2 Image        case  GLLV   Graylevel local variance  Pech2000                  LVar   stdfilt Image  ones WSize WSize    2          FM   std2 LVar  2       case  GLVN    Normalized GLV  Santos97          FM   std2 Image  2 mean2 Image        case  GRAE    Energy of gradient  Subbarao92a          Ix   Image          Iy   Image          Iy 1 end-1      diff Image  1  1           Ix   1 end-1    diff Image  1  2           FM   Ix  2   Iy  2          FM   mean2 FM        case  GRAT    Thresholded gradient  Snatos97          Th   0   Threshold         Ix   Image          Iy   Image          Iy 1 end-1      diff Image  1  1           Ix   1 end-1    diff Image  1  2           FM   max abs Ix   abs Iy            FM FM lt Th  0          FM   sum FM     sum sum FM  0         case  GRAS    Squared gradient  Eskicioglu95          Ix   diff Image  1  2           FM   Ix  2          FM   mean2 FM        case  HELM   Helmli s mean method  Helmli2001                  MEANF   fspecial  average   WSize WSize            U   imfilter Image  MEANF   replicate            R1   U  Image          R1 Image  0  1          index    U gt Image           FM   1  R1          FM index    R1 index           FM   mean2 FM        case  HISE    Histogram entropy  Krotkov86          FM   entropy Image        case  HISR    Histogram range  Firestone91          FM   max Image    -min Image            case  LAPE    Energy of laplacian  Subbarao92a          LAP   fspecial  laplacian            FM   imfilter Image  LAP   replicate    conv            FM   mean2 FM  2        case  LAPM    Modified Laplacian  Nayar89          M    -1 2 -1                   Lx   imfilter Image  M   replicate    conv            Ly   imfilter Image  M    replicate    conv            FM   abs Lx    abs Ly           FM   mean2 FM        case  LAPV    Variance of laplacian  Pech2000          LAP   fspecial  laplacian            ILAP   imfilter Image  LAP   replicate    conv            FM   std2 ILAP  2       case  LAPD    Diagonal laplacian  Thelen2009          M1    -1 2 -1           M2    0 0 -1 0 2 0 -1 0 0  sqrt 2           M3    -1 0 0 0 2 0 0 0 -1  sqrt 2           F1   imfilter Image  M1   replicate    conv            F2   imfilter Image  M2   replicate    conv            F3   imfilter Image  M3   replicate    conv            F4   imfilter Image  M1    replicate    conv            FM   abs F1    abs F2    abs F3    abs F4           FM   mean2 FM        case  SFIL   Steerable filters  Minhas2009            Angles    0 45 90 135 180 225 270 315           N   floor WSize 2           sig   N 2 5           x y    meshgrid -N N  -N N           G   exp - x  2 y  2   2 sig 2    2 pi sig           Gx   -x  G  sig 2  Gx   Gx sum Gx              Gy   -y  G  sig 2  Gy   Gy sum Gy              R     1    imfilter double Image   Gx   conv    replicate            R     2    imfilter double Image   Gy   conv    replicate            R     3    cosd 45  R     1  sind 45  R     2           R     4    cosd 135  R     1  sind 135  R     2           R     5    cosd 180  R     1  sind 180  R     2           R     6    cosd 225  R     1  sind 225  R     2           R     7    cosd 270  R     1  sind 270  R     2           R     7    cosd 315  R     1  sind 315  R     2           FM   max R    3           FM   mean2 FM        case  SFRQ    Spatial frequency  Eskicioglu95          Ix   Image          Iy   Image          Ix   1 end-1    diff Image  1  2           Iy 1 end-1      diff Image  1  1           FM   mean2 sqrt double Iy  2 Ix  2          case  TENG   Tenengrad  Krotkov86          Sx   fspecial  sobel            Gx   imfilter double Image   Sx   replicate    conv            Gy   imfilter double Image   Sx    replicate    conv            FM   Gx  2   Gy  2          FM   mean2 FM        case  TENV    Tenengrad variance  Pech2000          Sx   fspecial  sobel            Gx   imfilter double Image   Sx   replicate    conv            Gy   imfilter double Image   Sx    replicate    conv            G   Gx  2   Gy  2          FM   std2 G  2       case  VOLA    Vollath s correlation  Santos97          Image   double Image           I1   Image  I1 1 end-1      Image 2 end             I2   Image  I2 1 end-2      Image 3 end             Image   Image   I1-I2           FM   mean2 Image        case  WAVS   Sum of Wavelet coeffs  Yang2003           C S    wavedec2 Image  1   db6            H   wrcoef2  h   C  S   db6   1              V   wrcoef2  v   C  S   db6   1              D   wrcoef2  d   C  S   db6   1              FM   abs H    abs V    abs D           FM   mean2 FM        case  WAVV   Variance of  Wav    Yang2003           C S    wavedec2 Image  1   db6            H   abs wrcoef2  h   C  S   db6   1            V   abs wrcoef2  v   C  S   db6   1            D   abs wrcoef2  d   C  S   db6   1            FM   std2 H  2 std2 V  std2 D        case  WAVR           C S    wavedec2 Image  3   db6            H   abs wrcoef2  h   C  S   db6   1               V   abs wrcoef2  v   C  S   db6   1               D   abs wrcoef2  d   C  S   db6   1             A1   abs wrcoef2  a   C  S   db6   1            A2   abs wrcoef2  a   C  S   db6   2            A3   abs wrcoef2  a   C  S   db6   3            A   A1   A2   A3          WH   H  2   V  2   D  2          WH   mean2 WH           WL   mean2 A           FM   WH WL      otherwise         error  Unknown measure  s  upper Measure   end  end                                                                           function fm   AcMomentum Image   M N    size Image   Hist   imhist Image   M N   Hist   abs  0 255 -255 mean2 Image     Hist  fm   sum Hist   end                                                                      function fm   DctRatio M  MT   dct2 M   2  fm    sum MT    -MT 1 1   MT 1 1   end                                                                            function fm   ReRatio M  M   dct2 M   fm    M 1 2  2 M 1 3  2 M 2 1  2 M 2 2  2 M 3 1  2   M 1 1  2   end                                                                       A few examples of OpenCV versions      OpenCV port of  LAPM  algorithm  Nayar89  double modifiedLaplacian const cv  Mat amp  src        cv  Mat M    Mat  lt double gt  3  1   lt  lt  -1  2  -1       cv  Mat G   cv  getGaussianKernel 3  -1  CV 64F        cv  Mat Lx      cv  sepFilter2D src  Lx  CV 64F  M  G        cv  Mat Ly      cv  sepFilter2D src  Ly  CV 64F  G  M        cv  Mat FM   cv  abs Lx    cv  abs Ly        double focusMeasure   cv  mean FM  val 0       return focusMeasure        OpenCV port of  LAPV  algorithm  Pech2000  double varianceOfLaplacian const cv  Mat amp  src        cv  Mat lap      cv  Laplacian src  lap  CV 64F        cv  Scalar mu  sigma      cv  meanStdDev lap  mu  sigma        double focusMeasure   sigma val 0  sigma val 0       return focusMeasure        OpenCV port of  TENG  algorithm  Krotkov86  double tenengrad const cv  Mat amp  src  int ksize        cv  Mat Gx  Gy      cv  Sobel src  Gx  CV 64F  1  0  ksize       cv  Sobel src  Gy  CV 64F  0  1  ksize        cv  Mat FM   Gx mul Gx    Gy mul Gy        double focusMeasure   cv  mean FM  val 0       return focusMeasure        OpenCV port of  GLVN  algorithm  Santos97  double normalizedGraylevelVariance const cv  Mat amp  src        cv  Scalar mu  sigma      cv  meanStdDev src  mu  sigma        double focusMeasure    sigma val 0  sigma val 0     mu val 0       return focusMeasure      No guarantees on whether or not these measures are the best choice for your problem  but if you track down the papers associated with these measures  they may give you more insight  Hope you find the code useful  I know I did

User · Answer

Building off of Nike s answer   Its straightforward to implement the laplacian based method with opencv   short GetSharpness char  data  unsigned int width  unsigned int height           assumes that your image is already in planner yuv or 8 bit greyscale     IplImage  in   cvCreateImage cvSize width height  IPL DEPTH 8U 1       IplImage  out   cvCreateImage cvSize width height  IPL DEPTH 16S 1       memcpy in- gt imageData data width height           aperture size of 1 corresponds to the correct matrix     cvLaplace in  out  1        short maxLap   -32767      short  imgData    short  out- gt imageData      for int i  0 i lt  out- gt imageSize 2  i                  if imgData i   gt  maxLap  maxLap   imgData i              cvReleaseImage  amp in       cvReleaseImage  amp out       return maxLap      Will return a short indicating the maximum sharpness detected  which based on my tests on real world samples  is a pretty good indicator of if a camera is in focus or not   Not surprisingly  normal values are scene dependent but much less so than the FFT method which has to high of a false positive rate to be useful in my application

User · Answer

i implemented it use fft in matlab and check histogram of the fft compute mean and std but also fit function can be done  fa    abs fftshift fft sharp img     fb   abs fftshift fft blured img      f1 20 log10 0 001 fa   f2 20 log10 0 001 fb    figure imagesc f1  title  org   figure imagesc f2  title  blur    figure hist f1    100  title  org   figure hist f2    100  title  blur    mf1 mean f1      mf2 mean f2       mfd1 median f1      mfd2 median f2       sf1 std f1      sf2 std f2

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I came up with a totally different solution  I needed to analyse video still frames to find the sharpest one in every  X  frames  This way  I would detect motion blur and or out of focus images   I ended up using Canny Edge detection and I got VERY VERY good results with almost every kind of video  with nikie s method  I had problems with digitalized VHS videos and heavy interlaced videos    I optimized the performance by setting a region of interest  ROI  on the original image   Using EmguCV      Convert image using Canny using  Image lt Gray  byte gt  imgCanny   imgOrig Canny 225  175           Count the number of pixel representing an edge     int nCountCanny   imgCanny CountNonzero   0          Compute a sharpness grade         lt  1 5   blurred  in movement       de 1 5    6   acceptable        gt  6  stable  sharp     double dSharpness    nCountCanny   1000 0    imgCanny Cols   imgCanny Rows

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Answers above elucidated many things  but I think it is useful to make a conceptual distinction   What if you take a perfectly on-focus picture of a blurred image    The blurring detection problem is only well posed when you have a reference  If you need to design  e g   an auto-focus system  you compare a sequence of images taken with different degrees of blurring  or smoothing  and you try to find the point of minimum blurring within this set  I other words you need to cross reference the various images using one of the techniques illustrated above  basically--with various possible levels of refinement in the approach--looking for the one image with the highest high-frequency content

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Another very simple way to estimate the sharpness of an image is to use a Laplace  or LoG  filter and simply pick the maximum value  Using a robust measure like a 99 9  quantile is probably better if you expect noise  i e  picking the Nth-highest contrast instead of the highest contrast   If you expect varying image brightness  you should also include a preprocessing step to normalize image brightness contrast  e g  histogram equalization    I ve implemented Simon s suggestion and this one in Mathematica  and tried it on a few test images     The first test blurs the test images using a Gaussian filter with a varying kernel size  then calculates the FFT of the blurred image and takes the average of the 90  highest frequencies   testFft img      Table         blurred   GaussianFilter img  r      fft   Fourier ImageData blurred        w  h    Dimensions fft      windowSize   Round w 2 1      Mean Flatten  Abs         fft  w 2 - windowSize    w 2   windowSize            h 2 - windowSize    h 2   windowSize              r  0  10  0 5     Result in a logarithmic plot     The 5 lines represent the 5 test images  the X axis represents the Gaussian filter radius  The graphs are decreasing  so the FFT is a good measure for sharpness    This is the code for the  highest LoG  blurriness estimator  It simply applies an LoG filter and returns the brightest pixel in the filter result   testLaplacian img      Table         blurred   GaussianFilter img  r      Max Flatten ImageData LaplacianGaussianFilter blurred  1             r  0  10  0 5     Result in a logarithmic plot     The spread for the un-blurred images is a little better here  2 5 vs 3 3   mainly because this method only uses the strongest contrast in the image  while the FFT is essentially a mean over the whole image  The functions are also decreasing faster  so it might be easier to set a  blurry  threshold

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One way which I m currently using measures the spread of edges in the image   Look for this paper    ARTICLE Marziliano04perceptualblur      author    Pina Marziliano and Frederic Dufaux and Stefan Winkler and Touradj Ebrahimi       title    Perceptual blur and ringing metrics  Application to JPEG2000     Signal Process       journal    Image Commun       year    2004       pages    163--172      It s usually behind a paywall but I ve seen some free copies around   Basically  they locate vertical edges in an image  and then measure how wide those edges are    Averaging the width gives the final blur estimation result for the image   Wider edges correspond to blurry images  and vice versa   This problem belongs to the field of no-reference image quality estimation   If you look it up on Google Scholar  you ll get plenty of useful references   EDIT  Here s a plot of the blur estimates obtained for the 5 images in nikie s post   Higher values correspond to greater blur   I used a fixed-size 11x11 Gaussian filter and varied the standard deviation  using imagemagick s convert command to obtain the blurred images      If you compare images of different sizes  don t forget to normalize by the image width  since larger images will have wider edges    Finally  a significant problem is distinguishing between artistic blur and undesired blur  caused by focus miss  compression  relative motion of the subject to the camera   but that is beyond simple approaches like this one   For an example of artistic blur  have a look at the Lenna image  Lenna s reflection in the mirror is blurry  but her face is perfectly in focus   This contributes to a higher blur estimate for the Lenna image

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Thanks nikie for that great Laplace suggestion  OpenCV docs pointed me in the same direction  using python  cv2  opencv 2 4 10   and numpy     gray   cv2 cvtColor img  cv2 COLOR BGR2GRAY  numpy max cv2 convertScaleAbs cv2 Laplacian gray image 3     result is between 0-255   I found anything over 200ish is very in focus  and by 100  it s noticeably blurry   the max never really gets much under 20 even if it s completely blurred

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Matlab code of two methods that have been published in highly regarded journals  IEEE Transactions on Image Processing  are available here  https   ivulab asu edu software  check the CPBDM and JNBM algorithms  If you check the code it s not very hard to be ported and incidentally it is based on the Marzialiano s method as basic feature

[image-processing] Is there a way to detect if an image is blurry?

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