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Digital Image Restoration: Techniques and Models

This resource provides an overview of digital image restoration techniques and models, including environmental models, image degradation models, and restoration filters. It covers both subjective and objective measures of image enhancement and restoration. The material includes demonstrations and examples to help understand the concepts.

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Digital Image Restoration: Techniques and Models

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  1. Digital Image ProcessingECE.09.452/ECE.09.552Fall 2009 Lecture 5October 12, 2009 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall09/dip/

  2. Plan • Digital Image 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

  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 2: Spatial & Spectral Filtering http://engineering.rowan.edu/~shreek/fall09/dip/lab2.html

  15. Summary

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