1. Digital Image Processing 0909.452.01/0909.552.01 Fall 2003
Lecture 6 October 13, 2003
Shreekanth Mandayam
ECE Department
Rowan University

2. 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

3. DIP: Details

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

5. 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

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

7. 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/

8. Degradation & Restoration Examples: Gonzalez & Woods
Atmospheric Turbulence Model

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

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

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

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

13. Lab 3: Digital Image Restoration

14. Summary