image restoration · 2020. 10. 28. · image restoration, constrained least squares filtering cse...
TRANSCRIPT
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Image Restoration
Image Processing
CSE 166
Lecture 8
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Announcements
• Assignment 3 is due Nov 2, 11:59 PM
• Quiz 3 is Nov 4
• Assignment 4 will be released Nov 2
– Due Nov 9, 11:59 PM
• Reading
– Chapter 5: Image Restoration and Reconstruction
• Sections 5.1, 5.2, 5.3, 5.4, 5.6, and 5.7
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Model of image degradation
• Spatial domain
• Frequency domain
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Noiseimage
Originalimage
Degradedimage
Degradationfunction
Noiseimage
Originalimage
Degradedimage
Degradationfunction
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Model of image degradation, then restoration
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Estimate of original imageOriginal image
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Noise modeled as different probability density functions
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Adding noise from different models
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Gaussian Rayleigh Gamma
Free ofnoise
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Adding noise from different models
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Exponential Uniform Salt andpepper
Free ofnoise
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Histograms of sample patches
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Sample “flat” patches from images with noise
Identify closest probability density function (pdf) match:
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Gaussian Rayleigh Uniform
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Mean filters
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Additive Gaussian
noise
Geometric mean
filtered
Arithmetic mean
filtered
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X-ray image
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Mean filters
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Additivesalt
noise
Additivepeppernoise
Contraharmonicmean filtered
Contraharmonicmean filtered
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Order-statistic filters
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Additivesalt and peppernoise
1x median filtered
3x median filtered
2x median filtered
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Order-statistic filters
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Min filtered
Max filtered
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Comparing filters
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Alpha-trimmed mean filtered
Median filtered
Arithmetricmean
filtered
Geometric mean filtered
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Additive uniform +
salt and peppernoise
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Adaptive filters
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AdditiveGaussian
noise
Arithmetricmean filtered
Geometric mean
filtered
Adaptive noise reduction
filtered
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Adaptive filters
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Additivesalt and pepper
noiseMedian filtered
Adaptive median filtered
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Periodic noise
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Conjugate impulses
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Additive sinusoidal noise
DFT magnitude
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Notch reject filters
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Notch reject filter
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Filter in frequency
domain
Estimate of original
image
Degraded image
DFT magnitudeConjugate
impulses
Conjugate impulses
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Notch reject filter
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Filter in frequency
domain
Estimate of original
image
Degraded image
DFT magnitude
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Estimating the degradation function
• Methods
– Observation
– Experimentation
– Mathematical modeling
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Estimation of degradation function by experimentation
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Imaged (degraded) impulseImpulse of light
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Estimation of degradation function by mathematical modeling
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Atmospheric turbulence
model
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Estimation of degradation function by mathematical modeling
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Motion blur model
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Image restoration
• Inverse filtering
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Image restoration, inverse filtering
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FullLimited to
radius of 40
Limited to radius of 70
Limited to radius of 85
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Image restoration, Wiener filtering
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Inverse filtering Wiener filtering
Full Radially limited
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Image restoration, constrained least squares filtering
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Inversefiltering
Wienerfiltering
Degraded image
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Motion blur and
additive noise
Constrained least squares filtering
Less noise
Much less noise
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Next Lecture
• Color image processing
• Reading
– Chapter 7: Color Image Processing
• Sections 7.1, 7.2, 7.3, 7.4, 7.5, and 7.6
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