Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration...
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![Page 1: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/1.jpg)
Lecture 10 Image restoration and reconstruction
1. Basic concepts about image degradation/restoration
2. Noise models
3. Spatial filter techniques for restoration
![Page 2: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/2.jpg)
Image Restoration
• Image restoration is to recover an image that has been degraded by using a priori knowledge of the degradation phenomenon
• Image enhancement vs. image restoration– Enhancement is for vision– Restoration is to recover the original image– There is overlap of the techniques used
• Image restored is an approximation of the original image– Criteria for the goodness
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The model of Image Degradation
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G u v H u v F u v N u v
![Page 4: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/4.jpg)
Noise models
• Noise often arise during image acquisition/transformation– Caused by many factors– Spatial noise– Frequency noise
• Some important noise probability density functions– Gaussian noise– Rayleigh noise– Erlang (gamma) noise– Exponential noise– Uniform– Impulse
• Periodic noise
![Page 5: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/5.jpg)
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![Page 6: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/6.jpg)
Generate spatial noise of a given distribution
Theorem
Given CDF F(z). Let w be the uniform random number generator on (0,1). Then the random number has the CDF F(z)
Example: Reyleigh’s CDF is
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Matlab example: a = 50, b =10, M = 100, N = 100;R = a + sqrt(-b*log(1-rand(M,N)));
MatLab example 2: Gaussian distribution mean a and std ba = 10, b =10, M = 100, N = 100;R = a + b*randn(M,N);
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Add spatial noise to an image of
• Let f(x, y) be an M N image, and N(x, y) be the random MN noise of the given distribution. Then the image with the spatial noise is g(x, y) = f(x,y) + N(x,y)
MatLab example: f = imread('moon.tif');[M N] = size(f);s = uint8(a + sqrt(-b*log(1-rand(M,N))));fs = y + s;imshow(fs)
![Page 8: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/8.jpg)
![Page 9: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/9.jpg)
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Estimation of Noise Parameters
• Parameters of a PDF: mean, standard deviation, variance, moments about the mean
• The method of estimation– If possible, take a flat image the system and compute its
parameter– If only images are available.
Take a strip image S. Determine the histogram of S. Let denote the frequency of value zi
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Spatial filters based restoration technique
• When only additive random noise is present, spatial filter can be applied
• Mean filters – Arithmetic mean filter
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![Page 14: Lecture 10 Image restoration and reconstruction 1.Basic concepts about image degradation/restoration 2.Noise models 3.Spatial filter techniques for restoration.](https://reader036.fdocuments.us/reader036/viewer/2022062313/56649f445503460f94c6570a/html5/thumbnails/14.jpg)
Spatial filters based restoration technique
– Geometric mean filter
– Harmonic mean filter
– Contraharmonic mean filter
1
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reducing salt-and-pepper noise.
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