Image processing 有没有办法检测图像是否模糊？
我想知道是否有办法通过分析图像数据来确定图像是否模糊。是的，确实如此。计算快速傅里叶变换并分析结果。傅里叶变换告诉您图像中存在哪些频率。如果存在少量的高频，则图像模糊 定义术语“低”和“高”取决于您 编辑： 如评论中所述，如果您想要一个表示给定图像模糊度的单个浮点数，则必须制定一个合适的度量 提供这样一个度量标准。使用拉普拉斯内核卷积图像：Image processing 有没有办法检测图像是否模糊？,image-processing,opencv,Image Processing,Opencv,我想知道是否有办法通过分析图像数据来确定图像是否模糊。是的，确实如此。计算快速傅里叶变换并分析结果。傅里叶变换告诉您图像中存在哪些频率。如果存在少量的高频，则图像模糊 定义术语“低”和“高”取决于您 编辑： 如评论中所述，如果您想要一个表示给定图像模糊度的单个浮点数，则必须制定一个合适的度量 提供这样一个度量标准。使用拉普拉斯内核卷积图像： 1 1 -4 1 1 并对输出使用一个健壮的最大度量，以获得一个可用于阈值化的数字。在计算拉普拉斯算子之前，尽量避免对图像进行过多的平滑处理
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1 -4 1
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并对输出使用一个健壮的最大度量，以获得一个可用于阈值化的数字。在计算拉普拉斯算子之前，尽量避免对图像进行过多的平滑处理，因为您只会发现平滑后的图像确实模糊：-）。另一种非常简单的估计图像清晰度的方法是使用拉普拉斯（或对数）滤波器，然后简单地选择最大值。如果期望噪声（即，选择第n个最高对比度而不是最高对比度），则使用99.9%分位数等稳健度量可能更好。如果期望图像亮度变化，则还应包括预处理步骤以规范化图像亮度/对比度（例如直方图均衡化） 我已经在Mathematica中实现了Simon的建议和这个建议，并在一些测试图像上进行了尝试： 第一个测试使用具有不同内核大小的高斯滤波器模糊测试图像，然后计算模糊图像的FFT，并取90%最高频率的平均值：
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}]
testLaplacian[img_] := Table[
(
blurred = GaussianFilter[img, r];
Max[Flatten[ImageData[LaplacianGaussianFilter[blurred, 1]]]];
), {r, 0, 10, 0.5}]
在这里，未模糊图像的扩散稍微好一些（2.5 vs 3.3），主要是因为该方法仅使用图像中最强的对比度，而FFT基本上是整个图像的平均值。函数的衰减速度也更快，因此设置“模糊”阈值可能更容易。在使用自动对焦镜头时，我遇到了这组非常有用的算法。它是在MATLAB中实现的，但大多数函数很容易移植到OpenCV 它基本上是许多焦点测量算法的概览实现。如果您想阅读原始论文，代码中提供了算法作者的参考资料。Pertuz等人（SFF）在2012年发表的论文中对所有这些测量方法及其性能（应用于SFF的速度和精度）进行了详细的回顾 编辑：添加MATLAB代码以防链接死掉。
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<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>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
%******************************************************************
function FM=fmeasure（图像、测量、ROI）
%此函数用于测量图像的相对聚焦程度
%图像。它可以被调用为：
%
%FM=fmeasure（图像、方法、ROI）
%
%在哪里
%图像，是灰度图像，FM是计算出的
%焦点值。
%方法，是作为字符串的焦点度量算法。
%有关焦点列表，请参阅“operators.txt”
%测量方法。
%ROI，将ROI成像为矩形[xo-yo宽度高度]。
%如果传递了一个空参数，则整个
%图像被处理。
%
%佩图兹说
%Abr/2010
如果~ i为空（ROI）
图像=imcrop（图像，ROI）；
结束
WSize=15；%本地窗口的大小（仅限于某些运算符）
开关上部（测量）
案例“ACMO%”绝对中心力矩（Shirvaikar2004）
如果~isinteger（图像），图像=im2uint8（图像）；
结束
FM=Ac动量（图像）；
案例“布伦”'%Brenner's（Santos97）
[mn]=尺寸（图像）；
DH=图像；
DV=图像；
DH（1:M-2，：）=diff（图像，2,1）；
DV（：，1:N-2）=diff（图像，2,2）；
FM=最大值（DH，DV）；
FM=FM.^2；
FM=平均值2（FM）；
案例“CONT%”图像对比度（Nanda2001）
ImContrast=inline（'sum（abs（x（：）-x（5））））；
FM=nlfilter（图像，[3]，对比度）；
FM=平均值2（FM）；
案例“CURV%”图像曲率（Helmli2001）
如果~isinteger（图像），图像=im2uint8（图像）；
结束
M1=[-1011；-1011；-1011]；
M2=[1 01；1 01；1 01]；
P0=imfilter（图像，M1，‘复制’，‘conv’）/6；
P1=imfilter（图像、M1’、‘复制’、‘conv’）/6；
P2=3*imfilter（图像、M2、‘复制’、‘转换’）/10。。。
-imfilter（图像，M2'，复制''conv'）/5；
P3=-imfilter（图像，M2，'复制'，'转换'）/5。。。
+3*imfilter（图像，M2，'replicate'，'conv'）/10；
FM=abs（P0）+abs（P1）+abs（P2）+abs（P3）；
FM=平均值2（FM）；
案例“DCTE”%DCT能量比（Shen2006）
FM=nlfilter（图像，[8]，@DCT比率）；
FM=平均值2（FM）；
案例“DCTR”%DCT降低的能量比（Lee2009）
FM=nlfilter（图像，[8]，@ReRatio）；
FM=平均值2（FM）；
案例“GDER%”高斯导数（Geusebroek2000）
N=地板（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/总和（Gx（：）；
Gy=-y*G/（sig^2）；Gy=Gy/总和（Gy（：）；
Rx=imfilter（双（图像）、Gx、“conv”、“replicate”）；
Ry=imfilter（双重（图像），Gy，'conv'，'replicate'）；
FM=Rx.^2+Ry.^2；
FM=平均值2（FM）；
案例“GLVA%”灰度方差（Krotkov86）
FM=std2（图像）；
案例“GLLV%”灰度局部方差（Pech2000）
LVar=stdfilt（图像，个（WSize，WSize））。^2；
FM=std2（LVar）^2；
案例“GLVN%”规范化GLV（Santos97）
FM=std2（图像）^2/me
// OpenCV port of 'LAPM' algorithm (Nayar89)
double modifiedLaplacian(const cv::Mat& src)
{
cv::Mat M = (Mat_<double>(3, 1) << -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& 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& 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& src)
{
cv::Scalar mu, sigma;
cv::meanStdDev(src, mu, sigma);
double focusMeasure = (sigma.val[0]*sigma.val[0]) / mu.val[0];
return focusMeasure;
}
@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} }
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->imageData,data,width*height);
// aperture size of 1 corresponds to the correct matrix
cvLaplace(in, out, 1);
short maxLap = -32767;
short* imgData = (short*)out->imageData;
for(int i =0;i<(out->imageSize/2);i++)
{
if(imgData[i] > maxLap) maxLap = imgData[i];
}
cvReleaseImage(&in);
cvReleaseImage(&out);
return maxLap;
}
//Convert image using Canny
using (Image<Gray, byte> imgCanny = imgOrig.Canny(225, 175))
{
//Count the number of pixel representing an edge
int nCountCanny = imgCanny.CountNonzero()[0];
//Compute a sharpness grade:
//< 1.5 = blurred, in movement
//de 1.5 à 6 = acceptable
//> 6 =stable, sharp
double dSharpness = (nCountCanny * 1000.0 / (imgCanny.Cols * imgCanny.Rows));
}
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(:));
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))
for x in range(width - 1):
for x in range(0, width - 1, 10):
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];