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S. Mandayam/ DIP/ECE Dept./Rowan University Digital Image Processing 0909.452.01/0909.552.01 Fall 2003 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall03/dip/ Lecture 5 October 6, 2003
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S. Mandayam/ DIP/ECE Dept./Rowan UniversityPlan Digital Image 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) Continue Lab 2: Spatial & Spectral Filtering
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S. Mandayam/ DIP/ECE Dept./Rowan University DIP: Details
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S. Mandayam/ DIP/ECE Dept./Rowan University Image Preprocessing Enhancement Restoration Spatial Domain Spectral Domain Point Processing >>imadjust >>histeq Spatial filtering >>filter2 Filtering >>fft2/ifft2 >>fftshift Inverse filtering Wiener filtering
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S. Mandayam/ DIP/ECE Dept./Rowan University 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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S. Mandayam/ DIP/ECE Dept./Rowan University Degradation Model f(x,y) h(x,y) g(x,y) n(x,y) Degradation Model: g = h*f + n demos/demo5blur_invfilter/ demos/demo5blur_invfilter/degrade.m
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S. Mandayam/ DIP/ECE Dept./Rowan University Restoration Model f(x,y) Degradation Model f(x,y) Restoration Filter Unconstrained Constrained Inverse Filter Pseudo-inverse Filter Wiener Filter demos/demo5blur_invfilter/
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S. Mandayam/ DIP/ECE Dept./Rowan UniversityApproach demos/demo5blur_invfilter/ f(x,y) Build degradation model Formulate restoration algorithms f(x,y) Analyze using algebraic techniques Implement using Fourier transforms 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)
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S. Mandayam/ DIP/ECE Dept./Rowan University Lab 2: Spatial & Spectral Filtering http://engineering.rowan.edu/~shreek/fall03/dip/lab2.html
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S. Mandayam/ DIP/ECE Dept./Rowan UniversitySummary
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