Image Restoration

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 Bise n

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

3. 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.

4. 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.

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

6. 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

7. 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.

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

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

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

11. 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.

12. 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.

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

14. Midpoint Filter Good for random Gaussian and uniform noise

15. Result

16. 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

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

18. 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.

19. Thank You!!