6restoration - salt-and-pepper noise, and does not cause...

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6. Image Restoration Chapter 5 (Image Restoration, 5.1-5.3, 5.5-5.8)
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Image Restoration • A process that attempts to reconstruct or recover an image that has been degraded by using some a priori knowledge of the degradation phenomenon.
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Degradation Model • The degraded image is obtained by passing an input image through a degradation operator , H, together with an additive noise , • • Normally H is assumed to be a linear shift-invariant (LSI) system. So, ) , ( 2 1 n n y ) , ( 2 1 n n
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Mean Filters
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Mean Filters • Arithmetic mean filter reduces noise, but causes blurring. • Geometric mean filter causes less blurring. • Harmonic mean filter works well for salt noise, but fails for pepper noise. It does well also with other types of noise like Gaussian noise. • Contraharmonic mean filter is well
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Order-Statistics Filters
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Order-Statistics Filters • Median filter is the most popular in image processing. It is effective for
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Unformatted text preview: salt-and-pepper noise, and does not cause blurring. Max filter is useful for finding the brightest points in an image. It also reduces pepper noise. Min filter is useful for finding the darkest points an image It also reduces salt noise Max & Min Filtering Typical Point Spread Function of Blurring Examples of Point Spread Function Blurring: small exponential, large exponential, small Sinc 2 , large Sinc 2 Motion Blurring Image Blurred by Uniform Motion Inverse Filtering Pseudo Inverse Filtering Examples of Inverse Filtering Wiener Filtering Wiener Filtering Wiener Filtering Wiener Filtering General Case of Wiener Filtering Relation with Inverse Filtering Wiener Filtering Wiener Filtering Comparison of Inverse Filtering and Wiener Filtering Comparison of Inverse Filtering and Wiener Filtering Comparison...
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6restoration - salt-and-pepper noise, and does not cause...

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