Digital Image Processing 0909.452.01/0909.552.01 Fall 2003 Lecture 6 October 13, 2003 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall03/dip/

Plan Digital Image Restoration Lab 3: Digital Image Restoration Recall: 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: Digital Image 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

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) Unconstrained Constrained 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/

Degradation & Restoration Examples: Gonzalez & Woods Atmospheric Turbulence Model

Degradation & Restoration Examples: Gonzalez & Woods Example 5.11: Inverse Filtering

Degradation & Restoration Examples: Gonzalez & Woods Example 5.12: Wiener Filtering

Degradation & Restoration Examples: Gonzalez & Woods Example 5.10: Planar Motion Model

Degradation & Restoration Examples: Gonzalez & Woods Example 5.13: Inverse and Wiener Filtering

Lab 3: Digital Image Restoration http://engineering.rowan.edu/~shreek/fall03/dip/lab3.html

Summary