Image Restoration and its techniques in digital image processing and the filters used for it. And Applications of Image Restoration.

1. Image Restoration
-Case Study
Srishti Kakade
Shubham Patil
Vishwas Bisen

2. Contents
• What is image Restoration
• Restoration Techniques
• Filters used for restoration
• Results
• Advance image restoration
• Applications of image restoration
• Conclusion

4. • Image restoration attempts to restore images
that have been degraded
• Identify the degradation process and attempt
to reverse it.
• Almost Similar to image enhancement, but
more objective.

5. Image restoration
• Image restoration assumes a degradation model that is
known or can be estimated.
• Original content and quality does not mean Good
looking or appearance.
• Image restoration try to recover original image from
degraded with prior knowledge of degradation process.
• Restoration involves modeling of degradation and
applying the inverse process in order to recover the
original image.
• Although the restore image is not the original image,
its approximation of actual image.

6. Restoration Techniques.
• Inverse Filtering.
• Minimum Mean Squares Errors.
• Weiner Filtering.
• Constrained Least Square Filter.
• Non linear filtering.
• Advanced Restoration Technique.

7. WIENER FILTER
• Considering we need to design a wiener filter
in frequency domain as W(u , v)
• Restored image will be given as;
• Xn(u , v) = W(u , v).Y(u , v)
• Where Y(u , v) is the received signal and
Xn(u , v) is the restored image

8. WIENER FILTER
• Wiener filter is an excellent filter when it
comes to noise reduction or deblurring of
images.
• A user can test the performance of a wiener
filter for different parameters to get the
desired results.
• It is also used in steganography processes.
• It considers both the degradation function and
noise as part of analysis of an image.

9. Filter used for Restoration Process
• Mean filters
• Median filter
• Max and min filters
• Mid-point filter
• Adaptive filters
• Adaptive local noise reduction filter.

10. Filtering to Remove Noise
This is implemented as the simple smoothing
filter Blurs the image to remove noise.

11. Filtering to Remove Noise
• Achieves similar smoothing to the arithmetic
mean, but tends to lose less image detail.

12. Order Statistics Filters
• Spatial filters that are based on ordering the
pixel values that make up the neighborhood
operated on by the filter
• Median filter.
• Maximum and Minimum filter.
• Midpoint filter.
• Alpha trimmed mean filter.

13. Median Filter
• Excellent at noise removal, without the
smoothing effects that can occur with other
smoothing filters.
• Best result for removing salt and pepper
noise.

14. Maximum and Minimum Filter
• Max filter is good for pepper noise and min is
good for salt noise.

15. Midpoint Filter
• Good for random Gaussian and uniform noise

16. Result

17. Advanced Image Restoration
Adaptive Processing
• Spatial adaptive
• Frequency adaptive
Nonlinear Processing
• Thresholding, coring
• Iterative restoration
Advanced Transformation / Modelling
• Advanced image transforms, e.g., wavelet …
• Statistical image modelling
Blind Deblurring or Deconvolution

18. Applications of Digital Image
Processing
• Identification.
• Computer Vision or Robot vision.
• Steganography.
• Image Enhancement.
• Image Analysis in Medical.
• Morphological Image Analysis.
• Space Image Analysis.

19. What we learnt…
• Restore the original image from degraded image, if u
have clue about degradation function, is called image
restoration.
• The main objective should be estimate the degradation
function.
• If you are able to estimate the H, then follow the
inverse of degradation process of an image..
• Spatial domain techniques are particularly useful for
removing random noise.
• Frequency domain techniques are particularly useful
for removing periodic noise.

20. Thank You!!