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

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

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

Sample statistics (part 1)

Sample statistics (part 2)

Example

Context tree

Context tree construction

Test material

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

Impulsive noise

Content-dependent noise

Impulsive noise OriginalNoisy Context treeVector Median

Content-dependent noise OriginalNoisy Context treeVector Median

Example OriginalVector MedianContext tree

Impulsive noise OriginalVector MedianContext tree

Content-dependent noise OriginalVector MedianContext tree

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.