CoC is also associated with the concept of Bokeh, which I will discuss in a later post. It is sometimes known as the disk of confusion, circle of indistinctness, blur circle, or blur spot. The CoC occurs when the light rays passing through the lens are not perfectly focused. But optical blur is by no means shapeless, and has a lot to do with a concept known as the circle of confusion (CoC). Another way is through manually focusing a lens.Įither way, the result is optical blur. It also means that objects not in the focus plane will be blurred. Opening up the aperture to f/2.8 allows in more light, and the camera will compensate with the appropriate shutter speed. Blur occurs when there is a shallow depth of field. One way is by means of using the Aperture Priority setting on a camera. This can be achieved in a couple of ways. When part of a scene is blurry, then we consider this to be out-of-focus.
Gaussian blur imagej manual#
This is way easier than the manual focus that occurred in the ancient world of analog cameras. tDisplayRange(0.Most modern cameras automatically focus a scene before a photograph is acquired. tDisplayRange(0.0, maxIntensity) Ĭlij2.gaussianBlur2D(image_2, image_3, gaussianSigma, gaussianSigma) // Gaussian Blur2D tDisplayRange(0.0, maxIntensity) ĭef inputData = nvert(impOri, )ĭef filtered=ops.filter().gauss(inputData, as double)ĭef impIJ2 = ImageJFunctions.wrap(filtered, "IJ2 Gaussian Blur") // wraps the ImgPlus as an ImagePlus GaussianBlur.blurGaussian(impIJ1.getProcessor(), gaussianSigma) GaussianBlur gaussianBlur = new GaussianBlur() tRoi( (int)(imageSize/2-diameter/2), (int)(imageSize/2-diameter/2), diameter, diameter)
Create the default image, black background with a bright centerĭef impOri = IJ.createImage("Original", "16-bit black", imageSize, imageSize, 1) Compare the result of 2D Gaussian filter between ImageJ1, ImageJ2 and Clij2ĭef imageSize=200 // width and height of the imageĭef diameter=8 // diameter of the bright center on the black background imageĭef gaussianSigma = 100.0 // Gaussian sigma sizeĭef maxIntensity = 30.0 // Max intensity to display the result ImagePlus of the 3 gaussians filtersĭef backgroundValue= 0 //Image Background value Hi, you are right, it is easier with an example, here it is: // ops In other words, this error corresponds to about 10⁻⁶ of the value. For sigma=100, this pattern is about 10x above the numerical noise of the floating-point numbers. With the ImageJ 1.x implementation, at very high contrast, you will see indications of a square pattern caused by the upscaling, because the 3rd derivative is not smooth. The convolution with a Laplace kernel makes it obvious if the Gaussian or one of its derivatives is not smooth. You should see a sombrero function with nice circular symmetry, also if you crank up the contrast. Also the plot shows excellent agreement (it may help to define contrasting colors for the lines in More>Contents style).Īnother test for a Gaussian Blur algorithm (though not for the accuracy of the sigma value) is running a Laplace filter over it: Start with, e.g., a 2048×2048 float image and draw a single dot near the center and run Gaussian Blur. I have tried it with sigma = 10 and 100 and it is accurate within 5 digits. The ‘c’ fit parameter shown in the Log window is the sigma. You can use Data>Add Fit to see how well it corresponds to a Gaussian Function. Then “select all” and plot the (horizontal) profile. 2014×256, to avoid the influcence of the edges) and run a Gaussian Blur over it. You can easily verify the accuracy: Draw a single vertical line in a wide 32-bit floating point image (e.g. The additional blurring caused by the downscaling and upscaling is taken into account. For small sigma, it does the convolution as usual for large sigma (>8) it uses downscaling, does the convolution with a smaller kernel and then upscaling. Implementation: It makes use of the fact that the Gaussian Blur is separable in two subsequent operations in x&y.Edges: It assumes all out-of-image values are equal to the pixel value of the nearest Border.Since the implementation in plain ImageJ (ImageJ 1.x) is based on my code, I can only explain this one:
Gaussian blur imagej how to#
In addition, there is the problem of how to treat the edges, since the information outside of the image bounds is unknown. All Gaussian Blur implementations are approximations, because a Gaussian kernel has infinite extension.
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