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Digital Image Processing 0909.452.01/0909.552.01 Fall 2001
Lecture 7 October 22, 2001 Shreekanth Mandayam ECE Department Rowan University
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Plan Digital Image Restoration Lab 3: Degradation Models & Restoration
Enhancement vs. Restoration Environmental Models Image Degradation Model Image Restoration Model Point Spread Function (PSF) Models Linear Algebraic Restoration Unconstrained (Inverse Filter, Pseudoinverse Filter) Constrained (Wiener Filter, Kalman Filter) Lab 3: Degradation Models & Restoration
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DIP: Details
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Image Preprocessing Enhancement Restoration Inverse filtering
Wiener filtering Spatial Domain Spectral Domain Filtering >>fft2/ifft2 >>fftshift Point Processing >>imadjust >>histeq Spatial filtering >>filter2
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Enhancement vs. Restoration
“Better” visual representation Subjective No quantitative measures Remove effects of sensing environment Objective Mathematical, model dependent quantitative measures
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Degradation Model f(x,y) h(x,y) g(x,y) n(x,y) S
Degradation Model: g = h*f + n demos/demo5blur_invfilter/ demos/demo5blur_invfilter/degrade.m
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Restoration Model Degradation Restoration f(x,y) Model Filter f(x,y)
Constrained Unconstrained Inverse Filter Pseudo-inverse Filter Wiener Filter demos/demo5blur_invfilter/
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Approach f(x,y) Build degradation model g = h*f + n g = Hf + n
Formulate restoration algorithms Analyze using algebraic techniques Implement using Fourier transforms Approach g = h*f + n g = Hf + n W -1 g = DW -1 f + W -1 n f = H -1 g F(u,v) = G(u,v)/H(u,v) demos/demo5blur_invfilter/
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Lab 3: Degradation Models and Digital Image Restoration
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Summary
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