IEG4160_Part7 - IEG 4160: Image and Video Processing....

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Image Restoration IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu Introduction Image degradation/restoration model Noise models Restoration by spatial filtering Estimation of degradation functions Inverse filtering Wiener filtering Geometric transformation
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Page 2 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Introduction Objective of image restoration ± to recover a distorted image to the original form based on idealized models . The distortion is due to ± Image degradation in sensing environment e.g. random atmospheric turbulence ± Noisy degradation from sensor noise. ± Blurring degradation due to sensors e.g. camera motion or out-of-focus ± Geometric distortion e.g. earth photos taken by a camera in a satellite
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Page 3 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Introduction Enhancement Concerning the extraction of image features Difficult to quantify performance Subjective; making an image look better Restoration Concerning the restoration of degradation Performance can be quantified Objective; recovering the original image
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Page 4 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Image degradation/restoration model When H is a LSI system H ) , ( y x f + ) , ( y x η ) , ( y x g RF ) , ( ˆ y x f degradation restoration (,) gxy hxy f xy xy = ∗+ (,) (,) Guv HuvFuv Nuv = +
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Page 5 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Noise models Assuming degradation only due to additive noise ( H = 1) Noise from sensors ± Electronic circuits ± Light level ± Sensor temperature Noise from environment ± Lightening ± Atmospheric disturbance ± Other strong electric/magnetic signals
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Page 6 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Noise models Assuming that noise is ± independent of spatial coordinates, and ± uncorrelated with respect to the image content Gaussian noise ± Probability density function (PDF) ± z : gray level (Gaussian random variable) ± : mean of average value of z ± : standard deviation of z ± 22 () / 2 1 2 z pz e µ σ πσ −− = 2 : variance of z
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Page 7 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Noise models PDF of Gaussian noise ± 70% of z in ± 90% of z in [, ] µ σµ σ + [2 ,2 ] σµ σ +
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Page 8 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Noise models Impulse (salt-and-pepper) noise ± bipolar if ± unipolar if one of ± ± negative or positive; scaling is often necessary to form digital images ± extreme values occur (e.g. a = 0, b = 255) for ( ) for 0 otherwise a b P za pz P z b = = = 0, 0 ab P P and is 0 P P noise looks like salt-and-pepper granules if P P
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Page 9 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 7. Image Restoration Noise models Other common noise models ± Rayleigh noise ± Gamma noise ± Exponential noise ± Uniform noise
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Page 10 IEG 4160: Image and Video Processing.
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IEG4160_Part7 - IEG 4160: Image and Video Processing....

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