1 / 15

Digital Image Processing ECE.09.452/ECE.09.552 Fall 2007

Digital Image Processing ECE.09.452/ECE.09.552 Fall 2007. Lecture 6 October 29, 2007. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall07/dip/. Plan. Digital Image Restoration Recall: Environmental Models Image Degradation Model

qiana
Download Presentation

Digital Image Processing ECE.09.452/ECE.09.552 Fall 2007

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Digital Image ProcessingECE.09.452/ECE.09.552Fall 2007 Lecture 6October 29, 2007 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall07/dip/

  2. Plan • 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 Restoration Enhancement • Inverse filtering • Wiener filtering Spectral Domain Spatial Domain • Filtering • >>fft2/ifft2 • >>fftshift • Point Processing • >>imadjust • >>histeq • Spatial filtering • >>filter2

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

  6. S f(x,y) g(x,y) h(x,y) n(x,y) Degradation Model: g = h*f + n Degradation Model • demos/demo5blur_invfilter/ • demos/demo5blur_invfilter/degrade.m

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

  8. f(x,y) Build degradation model f(x,y) Analyze using algebraic techniques Formulate restoration algorithms Implement using Fourier transforms Approach g = h*f + n g = Hf + n W -1g = DW -1f + W -1n f = H -1g F(u,v) = G(u,v)/H(u,v) • demos/demo5blur_invfilter/

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

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

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

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

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

  14. Lab 3: Digital Image Restoration http://engineering.rowan.edu/~shreek/fall07/dip/lab3.html

  15. Summary

More Related