Digital Image Processing 0909.452.01/0909.552.01 Fall 2001 Lecture 7 October 22, 2001 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall01/dip/

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

DIP: Details

Image Preprocessing Enhancement Restoration Inverse filtering Wiener filtering Spatial Domain Spectral Domain Filtering >>fft2/ifft2 >>fftshift Point Processing >>imadjust >>histeq Spatial filtering >>filter2

Enhancement vs. Restoration “Better” visual representation Subjective No quantitative measures Remove effects of sensing environment Objective Mathematical, model dependent quantitative measures

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

Restoration Model Degradation Restoration f(x,y) Model Filter f(x,y) Constrained Unconstrained Inverse Filter Pseudo-inverse Filter Wiener Filter demos/demo5blur_invfilter/

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/

Lab 3: Degradation Models and Digital Image Restoration http://engineering.rowan.edu/~shreek/fall01/dip/lab3.html

Summary