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QB Ip

This document outlines topics related to image restoration and processing. It includes sections on image degradation models, inverse filtering, Wiener filtering, geometric transformations, interpolation methods, and approaches to image restoration including algebraic, inverse filtering, Wiener filtering, constrained and unconstrained restoration, and blind restoration. Specific questions cover 100% image restoration, defining rubber sheet transformations and degradation models, the principles of inverse and Wiener filtering, advantages of Wiener over inverse filtering, differences between Wiener and constrained least squares filtering, estimating degradation functions, and geometric and gray-level interpolation methods.

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Smita Sangewar
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43 views1 page

QB Ip

This document outlines topics related to image restoration and processing. It includes sections on image degradation models, inverse filtering, Wiener filtering, geometric transformations, interpolation methods, and approaches to image restoration including algebraic, inverse filtering, Wiener filtering, constrained and unconstrained restoration, and blind restoration. Specific questions cover 100% image restoration, defining rubber sheet transformations and degradation models, the principles of inverse and Wiener filtering, advantages of Wiener over inverse filtering, differences between Wiener and constrained least squares filtering, estimating degradation functions, and geometric and gray-level interpolation methods.

Uploaded by

Smita Sangewar
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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UNIT III

PART-A
1. 100% restoration of images is possible.Justify.
2. Why geometric transformations are called so?
3. Define image degradation model and sketch it.
4. Define rubber sheet transformation.
5. What is the principle of inverse filtering?
6. Why the image is subjected to Wiener filtering?
7. Define image restoration.
8. Give any two advantages of wiener filtering over inverse filtering.
9. Differentiate wiener filtering and constrained least squares filtering.
10. What is blind image restoration?
11. What are the ways to estimate the degradation function?
12. What is pseudo inverse filter?
13. Why geometric transformations are called rubber sheet transformations?
14. State the basic operations of geometric transformation.
15. What are tie points?
16. Mention the methods of gray- level interpolation?
17. State the concept behind nearest -neighbor interpolation.

PART-B
1. Explain the algebraic approach in image restoration.
2. What is the use of wiener filter in image restoration? Explain.
3. What is meant by inverse filtering? Explain.
4. Explain image degradation model /restoration process in detail.
5. What are the two approaches for blind image restoration? Explain in detail.
6. Explain in detail about unconstrained and constrained restoration.
7. Explain about removal of blur caused by uniform linear motion.
8. What is invariant degradation?Explain about estimating the degradation
function?
9. Write a note on Geometric mean Filter.
10. Discuss with mathematical model about unconstrained,constrained
restorations.
11. Draw the block diagram of Image restoration system & explain each block
critically?
12. What are the different mean filters used for restoration? Explain any one.
13. Explain the spatial transformation in DIP
14. Write in detail gray level interpolation based on the nearest neighbor
concept.
15. Explain Weiner filtering approach for image restoration(or) Write short
notes on minimum mean square error filtering.
16. Explain the concept of geometric transformation for image restoration?
17. What is gray level interpolation? Explain the schemes involved in it.

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