1. Filtering of map images by context tree modeling Pavel Kopylov and Pasi Fränti UNIVERSITY OF JOENSUU DEPARTMENT OF COMPUTER SCIENCE FINLAND

2. Noisy map images Original image (4 colors) Distorted image (1931 colors) Noise can originate from scanning, changing resolution, lossy JPEG compression.

3. Context-based filter Estimate pixel probability relatively to context Neighborhood configuration defined by a local template

4. Sample statistics (part 1)

5. Sample statistics (part 2)

6. Example

7. Context tree

8. Context tree construction

9. Test material

10. Experiments Apply impulsive or content-dependent noise to the original image. Apply filtering. Compare performance: Euclidean distance between two color samples in uniform L*a*b* (CIELAB) space

11. Impulsive noise

12. Content-dependent noise

13. Impulsive noise OriginalNoisy Context treeVector Median

14. Content-dependent noise OriginalNoisy Context treeVector Median

15. Example OriginalVector MedianContext tree

16. Impulsive noise OriginalVector MedianContext tree

17. Content-dependent noise OriginalVector MedianContext tree

18. Conclusions Capable of utilizing larger neighborhood than fixed-size template. The method outperforms vector median filter when noise level <25%. Tree construction requires extensive amount of memory; future work is needed to optimize this part.