Detecting scratch on image with much noise
I tried the following procedure for detection. The output looks moderate, but still I thought of sharing.
- downsample the color image.
apply median blur with different window sizes, then take the absolute difference: I'm doing this to enhance the scratch marks and at the same time achieve illumination flattening. Shown below are the difference images obtained this way.
use Gaussian Mixture based background/foreground segmentation to segment the scratch marks in the difference image. Idea here is, we can extract m x n windows from this image and train. As the scratch marks don't occupy a large area in the difference image, we can think the learned background should approximate the region outside scratch marks. This method worked better for both difference images than applying a threshold to the difference image. This method did not work well when I fed the downsampled image directly. I think this is due to the nonuniform nature of the pixel color values in regions. So I used the illumination flattened difference image. Below are the segmented images. This procedure is slow as it checks every possible m x n window in the image.
use probabilistic Hough transform to detect lines in the segmented image. Using the line density in regions or using morphological filtering for lines, I think it's possible to arrive at a reasonable guess as to where the scratch marks are.
Here's the code
background segmentation code:
Mat threshold_mog(Mat& im, Size window)
{
BackgroundSubtractorMOG2 bgModel;
Mat fgMask;
Mat output = Mat::ones(im.rows, im.cols, CV_8U);
for (int r = 0; r < im.rows - window.height; r++)
{
for (int c = 0; c < im.cols - window.width; c++)
{
bgModel.operator()(im(Rect(c, r, window.width, window.height)), fgMask);
}
}
for (int r = 0; r < im.rows - window.height; r++)
{
for (int c = 0; c < im.cols - window.width; c++)
{
Mat region = im(Rect(c, r, window.width, window.height));
bgModel.operator()(region, fgMask, 0);
fgMask.copyTo(output(Rect(c, r, window.width, window.height)));
}
}
return output;
}
main:
Mat rgb = imread("scratch_2.png.jpg");
pyrDown(rgb, rgb);
Mat med, med2, dif, bw;
medianBlur(rgb, med, 3);
medianBlur(rgb, med2, 21);
absdiff(med2, med, dif);
bw = threshold_mog(dif, Size(15, 15));
Mat dst = bw.clone();
vector<Vec4i> lines;
HoughLinesP(dst, lines, 1, CV_PI/180, 8, 10, 20);
for( size_t i = 0; i < lines.size(); i++ )
{
Vec4i l = lines[i];
line(rgb, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 1, CV_AA);
}
This is my implementation for the defect detection, its a very simple yet effective approach, i have implemented this code in MATLAB, but there is not any difficulty to port it on any language because its use basic image processing operations.
clc
clear all
close all
- Read Both the Images and downsample them(for fast calculation) by factor of 2.
im1 = imresize(imread('scratch.jpg'),0.5);
- Convert them into gray scale.
gray = rgb2gray(im);
- Apply a Gaussian filter of size 15 X 15.
gSize = 15;
gray = imfilter(gray,fspecial('gaussian',[gSize,gSize],gSize/2),'replicate');
- Findout Gradient Magnitude of the Images using Sobel mask.
[~,~,mg,~] = ImageFeatures.Gradients(gray);
- Threshold Gradient magnitude with a threshold of 30 percentile of max value.
`mgBw = mg > 0.3*max(mg(:));
- Apply morpholgical operation Closing of binary image by a disk mask of 3 X 3.
mgBw = imclose(mgBw,strel('disk',1));
- Apply Particle Analyze(CCL).
mgBw = bwareaopen(mgBw,500);
- Again Close Image for joining Lines together.
mgBw = imclose(mgBw,strel('disk',2));
- Fill Holes in the image.
mgBw = imfill(mgBw,'holes');
- Final Annotations:
Try Above procedure on your images hope it will work
Thank You
Values For Gaussian Mask are Given below i have just copied as it is, you can only use values 4 places after decimal and one more thing before convolution scale your image values between 0 and 1:
0.00253790859361804,0.00284879446220838,0.00314141610419987,0.00340305543986557,0.00362152753952273,0.00378611472031542,0.00388843599983945,0.00392315394879368,0.00388843599983945,0.00378611472031542,0.00362152753952273,0.00340305543986557,0.00314141610419987,0.00284879446220838,0.00253790859361804;
0.00284879446220838,0.00319776287779517,0.00352622975612324,0.00381991909245893,0.00406515334132644,0.00424990193722614,0.00436475725361032,0.00440372804277458,0.00436475725361032,0.00424990193722614,0.00406515334132644,0.00381991909245893,0.00352622975612324,0.00319776287779517,0.00284879446220838;
0.00314141610419987,0.00352622975612324,0.00388843599983945,0.00421229243210782,0.00448271658130972,0.00468644212981339,0.00481309512122034,0.00485606890058492,0.00481309512122034,0.00468644212981339,0.00448271658130972,0.00421229243210782,0.00388843599983945,0.00352622975612324,0.00314141610419987;
0.00340305543986557,0.00381991909245893,0.00421229243210782,0.00456312191696750,0.00485606890058492,0.00507676215263394,0.00521396370030743,0.00526051663974220,0.00521396370030743,0.00507676215263394,0.00485606890058492,0.00456312191696750,0.00421229243210782,0.00381991909245893,0.00340305543986557;
0.00362152753952273,0.00406515334132644,0.00448271658130972,0.00485606890058492,0.00516782273108746,0.00540268422664802,0.00554869395001131,0.00559823553262373,0.00554869395001131,0.00540268422664802,0.00516782273108746,0.00485606890058492,0.00448271658130972,0.00406515334132644,0.00362152753952273;
0.00378611472031542,0.00424990193722614,0.00468644212981339,0.00507676215263394,0.00540268422664802,0.00564821944786971,0.00580086485975791,0.00585265795345929,0.00580086485975791,0.00564821944786971,0.00540268422664802,0.00507676215263394,0.00468644212981339,0.00424990193722614,0.00378611472031542;
0.00388843599983945,0.00436475725361032,0.00481309512122034,0.00521396370030743,0.00554869395001131,0.00580086485975791,0.00595763557555571,0.00601082839853353,0.00595763557555571,0.00580086485975791,0.00554869395001131,0.00521396370030743,0.00481309512122034,0.00436475725361032,0.00388843599983945;
0.00392315394879368,0.00440372804277458,0.00485606890058492,0.00526051663974220,0.00559823553262373,0.00585265795345929,0.00601082839853353,0.00606449615428972,0.00601082839853353,0.00585265795345929,0.00559823553262373,0.00526051663974220,0.00485606890058492,0.00440372804277458,0.00392315394879368;
0.00388843599983945,0.00436475725361032,0.00481309512122034,0.00521396370030743,0.00554869395001131,0.00580086485975791,0.00595763557555571,0.00601082839853353,0.00595763557555571,0.00580086485975791,0.00554869395001131,0.00521396370030743,0.00481309512122034,0.00436475725361032,0.00388843599983945;
0.00378611472031542,0.00424990193722614,0.00468644212981339,0.00507676215263394,0.00540268422664802,0.00564821944786971,0.00580086485975791,0.00585265795345929,0.00580086485975791,0.00564821944786971,0.00540268422664802,0.00507676215263394,0.00468644212981339,0.00424990193722614,0.00378611472031542;
0.00362152753952273,0.00406515334132644,0.00448271658130972,0.00485606890058492,0.00516782273108746,0.00540268422664802,0.00554869395001131,0.00559823553262373,0.00554869395001131,0.00540268422664802,0.00516782273108746,0.00485606890058492,0.00448271658130972,0.00406515334132644,0.00362152753952273;
0.00340305543986557,0.00381991909245893,0.00421229243210782,0.00456312191696750,0.00485606890058492,0.00507676215263394,0.00521396370030743,0.00526051663974220,0.00521396370030743,0.00507676215263394,0.00485606890058492,0.00456312191696750,0.00421229243210782,0.00381991909245893,0.00340305543986557;
0.00314141610419987,0.00352622975612324,0.00388843599983945,0.00421229243210782,0.00448271658130972,0.00468644212981339,0.00481309512122034,0.00485606890058492,0.00481309512122034,0.00468644212981339,0.00448271658130972,0.00421229243210782,0.00388843599983945,0.00352622975612324,0.00314141610419987;
0.00284879446220838,0.00319776287779517,0.00352622975612324,0.00381991909245893,0.00406515334132644,0.00424990193722614,0.00436475725361032,0.00440372804277458,0.00436475725361032,0.00424990193722614,0.00406515334132644,0.00381991909245893,0.00352622975612324,0.00319776287779517,0.00284879446220838;
0.00253790859361804,0.00284879446220838,0.00314141610419987,0.00340305543986557,0.00362152753952273,0.00378611472031542,0.00388843599983945,0.00392315394879368,0.00388843599983945,0.00378611472031542,0.00362152753952273,0.00340305543986557,0.00314141610419987,0.00284879446220838,0.00253790859361804;
Sobel Mask:
1, 2, 1;
0, 0, 0;
-1,-2, 1;
and
1, 0,-1;
2, 0,-2;
1, 0,-1;
Sobel Gradient Magnitude Code(ImageFeatures.Gradient):
function [gx,gy,mag,phi] = Gradients(gray)
gray = double(gray);
horzmask = fspecial('sobel');
% vertmask = horzmask';
gx = imfilter(gray,horzmask,'replicate');
gy = imfilter(gray,horzmask','replicate');
phi = (atan2((gy),(gx)));
mag = mat2gray(sqrt(gx.^2+gy.^2));
end